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OMOTOSHO, LAWRENCE, KEHINDE SOTONWA, BENJAMIN ADEGOKE, OLUWASHINA OYENIRAN, and JOSHUA OYENIYI. "AN AUTOMATED SKIN DISEASE DIAGNOSTIC SYSTEM BASED ON DEEP LEARNING MODEL." Journal of Engineering Studies and Research 27, no. 3 (January 10, 2022): 43–50. http://dx.doi.org/10.29081/jesr.v27i3.287.
Повний текст джерелаАнотація:
The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.
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Kern, Bastian, and Patrick Jöckel. "A diagnostic interface for the ICOsahedral Non-hydrostatic (ICON) modelling framework based on the Modular Earth Submodel System (MESSy v2.50)." Geoscientific Model Development 9, no. 10 (October 13, 2016): 3639–54. http://dx.doi.org/10.5194/gmd-9-3639-2016.
Повний текст джерелаАнотація:
Abstract. Numerical climate and weather models have advanced to finer scales, accompanied by large amounts of output data. The model systems hit the input and output (I/O) bottleneck of modern high-performance computing (HPC) systems. We aim to apply diagnostic methods online during the model simulation instead of applying them as a post-processing step to written output data, to reduce the amount of I/O. To include diagnostic tools into the model system, we implemented a standardised, easy-to-use interface based on the Modular Earth Submodel System (MESSy) into the ICOsahedral Non-hydrostatic (ICON) modelling framework. The integration of the diagnostic interface into the model system is briefly described. Furthermore, we present a prototype implementation of an advanced online diagnostic tool for the aggregation of model data onto a user-defined regular coarse grid. This diagnostic tool will be used to reduce the amount of model output in future simulations. Performance tests of the interface and of two different diagnostic tools show, that the interface itself introduces no overhead in form of additional runtime to the model system. The diagnostic tools, however, have significant impact on the model system's runtime. This overhead strongly depends on the characteristics and implementation of the diagnostic tool. A diagnostic tool with high inter-process communication introduces large overhead, whereas the additional runtime of a diagnostic tool without inter-process communication is low. We briefly describe our efforts to reduce the additional runtime from the diagnostic tools, and present a brief analysis of memory consumption. Future work will focus on optimisation of the memory footprint and the I/O operations of the diagnostic interface.
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Blokh, D., N. Zurgil, I. Stambler, E. Afrimzon, Y. Shafran, E. Korech, J. Sandbank, and M. Deutsch. "An Information-theoretical Model for Breast Cancer Detection." Methods of Information in Medicine 47, no. 04 (2008): 322–27. http://dx.doi.org/10.3414/me0440.
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Summary Objectives: Formal diagnostic modeling is an important line of modern biological and medical research. The construction of a formal diagnostic model consists of two stages: first, the estimation of correlation between model parameters and the disease under consideration; and second, the construction of a diagnostic decision rule using these correlation estimates. A serious drawback of current diagnostic models is the absence of a unified mathematical methodological approach to implementing these two stages. The absence of aunified approach makesthe theoretical/biomedical substantiation of diagnostic rules difficult and reduces the efficacyofactual diagnostic model application. Methods: The present study constructs a formal model for breast cancer detection. The diagnostic model is based on information theory. Normalized mutual information is chosen as the measure of relevance between parameters and the patterns studied. The “nearest neighbor” rule is utilized for diagnosis, while the distance between elements is the weighted Hamming distance. The model concomitantly employs cellular fluorescence polarization as the quantitative input parameter and cell receptor expression as qualitative parameters. Results: Twenty-four healthy individuals and 34 patients (not including the subjects analyzed for the model construction) were tested by the model. Twenty-three healthy subjects and 34 patients were correctly diagnosed. Conclusions: The proposed diagnostic model is an open one,i.e.it can accommodate new additional parameters, which may increase its effectiveness.
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Zhu, Ning, Xiaoliang Xing, Limei Cao, Yingjun Zhang, Ti Zhang, Zhen Li, Fen Zou, and Qing Li. "Study on the Diagnosis of Gastric Cancer by Magnetic Beads Extraction and Mass Spectrometry." BioMed Research International 2020 (August 5, 2020): 1–8. http://dx.doi.org/10.1155/2020/2743060.
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Objective. This study constructed a model for the early diagnosis of gastric cancer by comparing the serum peptides profiles of patients with advanced gastric cancer and healthy people. And that model may be the potential to be applied for the efficacy evaluation and recurrence monitoring in gastric cancer. Methods. Serums of 30 healthy people and 30 advanced gastric cancer patients were matched by age and gender were collected. The serum peptide spectrum was obtained by MB-WCX concentration and MALDI-TOF MS analysis. Based on the analysis of the efficiency of differential peptides in the diagnosis of gastric cancer, we first established a model for the diagnosis of gastric cancer based on differential peptides and then carried out external verification. The diagnostic reliability of this model was further tested by compared with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9). Results. In this present study, we found the expression of two peptide peaks with a molecular weight of 2863 Da and 2953 Da were significantly increased in gastric cancer serum, while the expression of two peptide peaks with a molecular weight of 1945 Da and 2082 Da were significantly decreased. Depending on the characteristics of peptide expression, we constructed a diagnostic model, we compared the sensitivity and specificity of the model established by 2953 Da/1945 Da, and found this model is significantly higher than CEA and CA19-9. Conclusion. There were some differences in serum peptides profiles between patients with advanced gastric cancer and healthy people. The serum peptide diagnostic models based on 2953 Da and 1945 Da have high diagnostic efficiency for advanced gastric cancer. Our result indicated that this model was well worth further validation for clinical diagnosis.
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Al-Hasani, Maryam, Laith R. Sultan, Hersh Sagreiya, Theodore W. Cary, Mrigendra B. Karmacharya, and Chandra M. Sehgal. "Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis." Diagnostics 12, no. 11 (November 9, 2022): 2737. http://dx.doi.org/10.3390/diagnostics12112737.
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Objective: The study evaluates quantitative ultrasound (QUS) texture features with machine learning (ML) to enhance the sensitivity of B-mode ultrasound (US) for the detection of fibrosis at an early stage and distinguish it from advanced fibrosis. Different ML methods were evaluated to determine the best diagnostic model. Methods: 233 B-mode images of liver lobes with early and advanced-stage fibrosis induced in a rat model were analyzed. Sixteen features describing liver texture were measured from regions of interest (ROIs) drawn on B-mode images. The texture features included a first-order statistics run length (RL) and gray-level co-occurrence matrix (GLCM). The features discriminating between early and advanced fibrosis were used to build diagnostic models with logistic regression (LR), naïve Bayes (nB), and multi-class perceptron (MLP). The diagnostic performances of the models were compared by ROC analysis using different train-test sampling approaches, including leave-one-out, 10-fold cross-validation, and varying percentage splits. METAVIR scoring was used for histological fibrosis staging of the liver. Results: 15 features showed a significant difference between the advanced and early liver fibrosis groups, p < 0.05. Among the individual features, first-order statics features led to the best classification with a sensitivity of 82.1–90.5% and a specificity of 87.1–89.8%. For the features combined, the diagnostic performances of nB and MLP were high, with the area under the ROC curve (AUC) approaching 0.95–0.96. LR also yielded high diagnostic performance (AUC = 0.91–0.92) but was lower than nB and MLP. The diagnostic variability between test-train trials, measured by the coefficient-of-variation (CV), was higher for LR (3–5%) than nB and MLP (1–2%). Conclusion: Quantitative ultrasound with machine learning differentiated early and advanced fibrosis. Ultrasound B-mode images contain a high level of information to enable accurate diagnosis with relatively straightforward machine learning methods like naïve Bayes and logistic regression. Implementing simple ML approaches with QUS features in clinical settings could reduce the user-dependent limitation of ultrasound in detecting early-stage liver fibrosis.
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Wolff, Jamie K., Michelle Harrold, Tressa Fowler, John Halley Gotway, Louisa Nance, and Barbara G. Brown. "Beyond the Basics: Evaluating Model-Based Precipitation Forecasts Using Traditional, Spatial, and Object-Based Methods." Weather and Forecasting 29, no. 6 (December 1, 2014): 1451–72. http://dx.doi.org/10.1175/waf-d-13-00135.1.
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Abstract While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid- and coarse-resolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Method for Object-Based Diagnostic Evaluation (MODE) were utilized for this study with careful consideration given to how these methods were applied and how the results were interpreted. By illustrating the types of forecast attributes appropriate to assess with the spatial verification techniques, this paper provides examples of how to obtain advanced diagnostic information to help identify what aspects of the forecast are or are not performing well.
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Shubina, A. S., and L. M. Petrova. "Training Educational Psychologists: A Model of Working with Diagnostic Case." Psychological-Educational Studies 8, no. 3 (2016): 115–26. http://dx.doi.org/10.17759/psyedu.2016080311.
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The paper describes a model of working with a diagnostic case in educational psychological practice and analyses its compliance with the requirements of the professional standard for educational psychologists as well as with the theoretical bases of psychological assessment as a form of professional activity of a psychologist. The paper reviews the possibilities for making the requirements of the professional standard more specific by means of relating its components to the stages of the diagnostic process. As it is shown, a number of aspects in the diagnostic activity are deficient and require to be specially developed during professional and advanced training. The paper analyses the necessity of designing the content of psychodiagnostic disciplines so that they involve working with diagnostic hypotheses. It also outlines the tasks of mastering psychodiagnostic disciplines which, if solved successfully, would prevent students from making typical diagnostic mistakes. Finally, the paper discusses the difficulties with the development of the gnostic component of diagnostic activity in graduate students with bachelor degrees in a non-psychology field.
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La Spada, Luigi. "Metasurfaces for Advanced Sensing and Diagnostics." Sensors 19, no. 2 (January 16, 2019): 355. http://dx.doi.org/10.3390/s19020355.
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Interest in sensors and their applications is rapidly evolving, mainly driven by the huge demand of technologies whose ultimate purpose is to improve and enhance health and safety. Different electromagnetic technologies have been recently used and achieved good performances. Despite the plethora of literature, limitations are still present: limited response control, narrow bandwidth, and large dimensions. MetaSurfaces, artificial 2D materials with peculiar electromagnetic properties, can help to overcome such issues. In this paper, a generic tool to model, design, and manufacture MetaSurface sensors is developed. First, their properties are evaluated in terms of impedance and constitutive parameters. Then, they are linked to the structure physical dimensions. Finally, the proposed method is applied to realize devices for advanced sensing and medical diagnostic applications: glucose measurements, cancer stage detection, water content recognition, and blood oxygen level analysis. The proposed method paves a new way to realize sensors and control their properties at will. Most importantly, it has great potential to be used for many other practical applications, beyond sensing and diagnostics.
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Sakai, S., K. Kobayashi, J. Nakamura, S. Toyabe, and K. Akazawa. "Accuracy in the Diagnostic Prediction of Acute Appendicitis Based on the Bayesian Network Model." Methods of Information in Medicine 46, no. 06 (2007): 723–26. http://dx.doi.org/10.3414/me9066.
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Summary Objectives : The diagnosis of acute appendicitis is difficult, and a diagnostic error will often lead to either a perforation or the removal of a normal appendix. In this study, we constructed a Bayesian network model for the diagnosis of acute appendicitis and compared the diagnostic accuracy with other diagnostic models, such as the naive Bayes model, an artificial neural network model, and a logistic regression model. Methods : The data from 169 patients, who suffered from acute abdominal pain and who were suspected of having an acute appendicitis, were analyzed in this study. Nine variables were used for the evaluation of the accuracy of the four models for the diagnosis of an acute appendicitis. The naive Bayes model, the Bayesian network model, an artificial neural network model, and a logistic regression model were used i this study for the diagnosis of acute appendicitis. These four models were validated by using the “632 + bootstrap method” for resampling. The levels of accuracy of the four models for diagnosis were compared by the error rates and by the areas under the receiver operating characteristic curves. Results : Through the course of illness, 50.9% (86 of 169) of the patients were diagnosed as having an acute appendicitis. The error rate was the lowest in the Bayesian network model, as compared with the other diagnostic models. The area under the receiver operating characteristic curve analysis also showed that the Bayesian network model provided the most reliable results. Conclusion : The Bayesian network model provided the most accurate results in comparison to other models for the diagnosis of acute appendicitis.
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Schaefer, Inga-Marie, Ronald P. DeMatteo, and César Serrano. "The GIST of Advances in Treatment of Advanced Gastrointestinal Stromal Tumor." American Society of Clinical Oncology Educational Book, no. 42 (April 2022): 1–15. http://dx.doi.org/10.1200/edbk_351231.
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Gastrointestinal stromal tumor (GIST) is the most common malignant neoplasm of mesenchymal origin and a compelling clinical and biologic model for the rational development of molecularly targeted agents. This is because the majority of GISTs are driven by gain-of-function mutations in KIT or PDGFRA receptor tyrosine kinases. Specific GIST mutations circumscribe well-defined molecular subgroups that must be determined during the diagnostic work-up to guide clinical management, including therapeutic decisions. Surgery is the cornerstone treatment in localized disease and can also be clinically relevant in the metastatic setting. The correct combination and sequence of targeted agents and surgical procedures improves outcomes for patients with GIST and should be discussed individually within multidisciplinary expert teams. All currently approved agents for the treatment of GIST are based on orally available tyrosine kinase inhibitors targeting KIT and PDGFRA oncogenic activation. Although first-line imatinib achieves remarkable prolonged disease control, the benefit of subsequent lines of treatment is more modest. Novel therapeutic strategies focus on overcoming the heterogeneity of KIT or PDGFRA secondary mutations and providing more potent inhibition of specific challenging mutations. This article reviews the current understanding and treatment of GIST, with an emphasis on recent advances.
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Suleimenov, Batyrbek A., Laura A. Sugurova, Alibek B. Suleimenov, Aituar B. Suleimenov, and Oxana V. Zhirnova. "Synthesis of the equipment health management system of the turbine units' of thermal power stations." Mechanics & Industry 19, no. 2 (2018): 209. http://dx.doi.org/10.1051/meca/2017056.
Повний текст джерелаАнотація:
The aim of the research is the development of technical diagnostics subsystem with the possibility of its further integration into the automated system of equipment health management, which will improve the efficiency of data ware, hardware and software. Synthesis of intellectual diagnostic models was produced a by using the Matlab graphical agents. At the same time, there were synthesized models of three types: fuzzy, neural-network and model built by planning the full factorial experimental method. Was proposed the concept of the three-stage procedure of the diagnosis of the thermal power station's turbine unit, instead of the creation of diagnosis mathematical models and failure models of objects, immediately begin to develop an algorithm of diagnosis using advanced intelligent technologies. The technique of creating a sub-line diagnostics status of the turbine unit, which includes three main stages: identification of diagnostic features based on expert method; the synthesis of diagnostic model of the facility technical condition; research models on the stability, sensitivity and uniqueness, was proposed. The main diagnostic features of assessing the state of turbine equipment, which, in accordance with the concept developed, allow forming a matrix of planning a full factorial experiment. The proposed techniques and concepts were subjected to experimental verification. The intellectual diagnostic model of turbine unit equipment health was proposed, synthesized and investigated. It was found that the best model is the model, built using neuro-fuzzy algorithms. The simulation was provided for neuro-fuzzy algorithms and confirmed their effectiveness and compliance with the laws of the physical functioning of the HPC. The results of this research have been used in the development of Almaty CHP-2 turbine equipment health management subsystems, allow the further development of the theoretical foundations of intellectual systems, and demonstrate the possibility of using modern concepts to solve important technical problems. Subsystem of operative diagnosis and the following software implementation in a complex of automated technological process of thermal power control system allows one to make an early diagnosis of the equipment health. This significantly reduces the maintenance costs, improves reliability and security, as well as the effectiveness of the control system. In this regard, the results of this study provide further development of the theoretical foundations of the intellectual systems and demonstrate the possibility of modern concepts usage to determinate the important technical problems.
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Jiao, Yan, Yanqing Li, Peiqiang Jiang, Wei Han, and Yahui Liu. "PGM5: a novel diagnostic and prognostic biomarker for liver cancer." PeerJ 7 (June 11, 2019): e7070. http://dx.doi.org/10.7717/peerj.7070.
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Background Liver cancer is a common malignancy and a significant public health problem worldwide, but diagnosis and prognostic evaluation remain challenging for clinicians. Metabolic reprogramming is a hallmark of cancer, and we therefore examined the diagnostic and prognostic value of a metabolic enzyme, phosphoglucomutase-like protein 5 (PGM5), in liver cancer. Methods All data were from The Cancer Genome Atlas database. R and related statistical packages were used for data analysis. Hepatic PGM5 expression was determined in different groups, and the chi-squared test and Fisher’s exact test were used to determine the significance of differences. The pROC package was used to determine receiver operating characteristic (ROC) curves, the survival package was used to for survival analysis and development of a Cox multivariable model, and the ggplot2 package was used for data visualization. Results PGM5 expression was significantly lower in cancerous than adjacent normal liver tissues, and had modest diagnostic value based on ROC analysis and calculations of area under the curve (AUC). Hepatic PGM5 expression had positive associations with male sex and survival, but negative associations with advanced histologic type, advanced histologic grade, advanced stage, and advanced T classification. Patents with low PGM5 levels had poorer overall survival and relapse-free survival. PGM5 was independently associated with patient prognosis. Conclusion PGM5 has potential use as a diagnostic and prognostic biomarker for liver cancer.
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Lipinski, Piotr, Edyta Brzychczy, and Radoslaw Zimroz. "Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space." Sensors 20, no. 21 (October 22, 2020): 5979. http://dx.doi.org/10.3390/s20215979.
Повний текст джерелаАнотація:
Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.
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Yang, Mei, Lina Jiang, Yijin Wang, Xi Li, Zhengsheng Zou, Tao Han, Yuemin Nan, Fengmin Lu, and Jingmin Zhao. "Step layered combination of noninvasive fibrosis models improves diagnostic accuracy of advanced fibrosis in nonalcoholic fatty liver disease." Journal of Gastrointestinal and Liver Diseases 28, no. 3 (September 1, 2019): 289–96. http://dx.doi.org/10.15403/jgld-420.
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Background and Aims: Liver fibrosis is stage-dependently associated with non-alcoholic fatty liver disease (NAFLD) progression. The increased awareness of non-invasive diagnosis has led to the establishment of many fibrosis diagnosis models with various accuracies. We aimed to evaluate the diagnostic performance of nine clinical non-invasive fibrosis models in NAFLD and provide an optimal diagnostic method for advanced fibrosis by step layered combination of non-invasive models.
Methods: 453 consecutive patients with biopsy-proven NAFLD were enrolled from three centers and were divided into study cohort and validation cohort randomly. Aspartate aminotransferase-to-platelet ratio index (APRI), BARD, FiB-4, FibroMeter NAFLD, Forns’ Index, Hui model, non-invasive Koeln-Essen- index (NIKEI), S Index and NAFLD fibrosis score (NFS) were calculated. The high area under the receiver operating characteristic curve (AUROC) models were stepwise combined for further diagnosing NAFLD advanced fibrosis.
Results: All models had good performance with high negative predictive value (NPV) and specificity for diagnosing fibrosis, while positive predictive value (PPV) and sensitivity were low. APRI, BARD, FibroMeter NAFLD and NIKEI had higher AUROCs and their step layered combination for diagnosing advanced fibrosis showed high specificity, sensitivity, NPV and PPV up to 89.13%, 72.50%, 74.36%, and 88.17%, which also performed well in the validation cohort.
Conclusions: APRI, BARD, FibroMeter NAFLD and NIKEI had better diagnostic accuracy, and could be preferred for diagnosing NAFLD fibrosis. The step layered combination of these models performed much better than each single scoring system for diagnosing advanced fibrosis, provides valuable reference for clinical practice and might be a potential substitution of liver biopsy.
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Solazzo, Efisio, Christian Hogrefe, Augustin Colette, Marta Garcia-Vivanco, and Stefano Galmarini. "Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework." Atmospheric Chemistry and Physics 17, no. 17 (September 7, 2017): 10435–65. http://dx.doi.org/10.5194/acp-17-10435-2017.
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Abstract. The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America. The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition. To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance) of the base case and of the sensitivity runs are analysed in conjunction with timescale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration. The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study), allowing the detection of the timescale and the fields that the two models are most sensitive to. The representation of planetary boundary layer (PBL) dynamics is pivotal to both models. In particular, (i) the fluctuations slower than ∼ 1.5 days account for 70–85 % of the mean square error of the full (undecomposed) ozone time series; (ii) a recursive, systematic error with daily periodicity is detected, responsible for 10–20 % of the quadratic total error; (iii) errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network-average ozone observations in summer in both Europe and North America); (iv) the CMAQ ozone error has a weak/negligible dependence on the errors in NO2, while the error in NO2 significantly impacts the ozone error produced by Chimere; (v) the response of the models to variations of anthropogenic emissions and boundary conditions show a pronounced spatial heterogeneity, while the seasonal variability of the response is found to be less marked. Only during the winter season does the zeroing of boundary values for North America produce a spatially uniform deterioration of the model accuracy across the majority of the continent.
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Eyring, Veronika, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, et al. "Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP." Geoscientific Model Development 13, no. 7 (July 30, 2020): 3383–438. http://dx.doi.org/10.5194/gmd-13-3383-2020.
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Abstract. The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top–down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.
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Miller, R. A., and F. E. Masarie. "The Demise of the “Greek Oracle” Model for Medical Diagnostic Systems." Methods of Information in Medicine 29, no. 01 (1990): 1–2. http://dx.doi.org/10.1055/s-0038-1634767.
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Li, Huan Liang, Xiao Qiang Yang, Jin Xing Shen, and Liu Hai Chen. "Knowledge-Based Fault Diagnostic System Using Binary Fault Tree." Applied Mechanics and Materials 333-335 (July 2013): 1752–57. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1752.
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For the improvement of reliability, safety and efficiency advanced methods of fault diagnosis, new technology and method such as expert system become increasingly important for many kinds of fault diagnosis. Thereby, the classification principle of system structure is used to build the hierarchical analysis model of combustion engine. The model is converted to binary fault tree by means of Fault Tree Analytical Method (FTA). Meanwhile, the knowledge base is constructed with production rule and frame representation. Fault diagnosis system is designed on the thought of analytical hierarchy process. Its development is accomplished by Delphi 7.0 language. The work has offered a simple and practical tool to users and brings great convenience to engineering corps.
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Martus, P. "A Measurement Model of Disease Severity in Absence of a Gold Standard." Methods of Information in Medicine 40, no. 03 (2001): 265–71. http://dx.doi.org/10.1055/s-0038-1634164.
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Abstract:In the absence of a gold standard, we propose the use of confirmatory factor analysis for the quantification of agreement between diagnostic measurements and the true disease severity. The essential assumption is conditional independence of diagnostic measurements adjusted for the severity of the disease. However, depending on the number of measurements available, the method works even if some of them are conditionally dependent. We illustrate the method using an example related to glaucoma eye disease.
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Takahashi, Takamune, and Raymond C. Harris. "Role of Endothelial Nitric Oxide Synthase in Diabetic Nephropathy: Lessons from Diabetic eNOS Knockout Mice." Journal of Diabetes Research 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/590541.
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Diabetic nephropathy (DN) is the leading cause of end-stage renal disease in many countries. The animal models that recapitulate human DN undoubtedly facilitate our understanding of this disease and promote the development of new diagnostic markers and therapeutic interventions. Based on the clinical evidence showing the association of eNOS dysfunction with advanced DN, we and others have created diabetic mice that lack eNOS expression and shown that eNOS-deficient diabetic mice exhibit advanced nephropathic changes with distinct features of progressive DN, including pronounced albuminuria, nodular glomerulosclerosis, mesangiolysis, and arteriolar hyalinosis. These studies clearly defined a critical role of eNOS in DN and developed a robust animal model of this disease, which enables us to study the pathogenic mechanisms of progressive DN. Further, recent studies with this animal model have explored the novel mechanisms by which eNOS deficiency causes advanced DN and provided many new insights into the pathogenesis of DN. Therefore, here we summarize the findings obtained with this animal model and discuss the roles of eNOS in DN, unresolved issues, and future investigations of this animal model study.
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Zhang, Kunli, Linkun Cai, Yu Song, Tao Liu, and Yueshu Zhao. "Combining External Medical Knowledge for Improving Obstetric Intelligent Diagnosis: Model Development and Validation." JMIR Medical Informatics 9, no. 5 (May 10, 2021): e25304. http://dx.doi.org/10.2196/25304.
Повний текст джерелаАнотація:
Background Data-driven medical health information processing has become a new development trend in obstetrics. Electronic medical records (EMRs) are the basis of evidence-based medicine and an important information source for intelligent diagnosis. To obtain diagnostic results, doctors combine clinical experience and medical knowledge in their diagnosis process. External medical knowledge provides strong support for diagnosis. Therefore, it is worth studying how to make full use of EMRs and medical knowledge in intelligent diagnosis. Objective This study aims to improve the performance of intelligent diagnosis in EMRs by combining medical knowledge. Methods As an EMR usually contains multiple types of diagnostic results, the intelligent diagnosis can be treated as a multilabel classification task. We propose a novel neural network knowledge-aware hierarchical diagnosis model (KHDM) in which Chinese obstetric EMRs and external medical knowledge can be synchronously and effectively used for intelligent diagnostics. In KHDM, EMRs and external knowledge documents are integrated by the attention mechanism contained in the hierarchical deep learning framework. In this way, we enrich the language model with curated knowledge documents, combining the advantages of both to make a knowledge-aware diagnosis. Results We evaluate our model on a real-world Chinese obstetric EMR dataset and showed that KHDM achieves an accuracy of 0.8929, which exceeds that of the most advanced classification benchmark methods. We also verified the model’s interpretability advantage. Conclusions In this paper, an improved model combining medical knowledge and an attention mechanism is proposed, based on the problem of diversity of diagnostic results in Chinese EMRs. KHDM can effectively integrate domain knowledge to greatly improve the accuracy of diagnosis.
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Yang, Hua, Bing-Qing Xia, Bo Jiang, Guozhen Wang, Yi-Peng Yang, Hao Chen, Bing-Sheng Li, An-Gao Xu, Yun-Bo Huang, and Xin-Ying Wang. "Diagnostic Value of Stool Dna Testing for Multiple Markers Of Colorectal Cancer and Advanced Adenoma: A Meta-Analysis." Canadian Journal of Gastroenterology 27, no. 8 (2013): 467–75. http://dx.doi.org/10.1155/2013/258030.
Повний текст джерелаАнотація:
BACKGROUND AND OBJECTIVES: The diagnostic value of stool DNA (sDNA) testing for colorectal neoplasms remains controversial. To compensate for the lack of large-scale unbiased population studies, a meta-analysis was performed to evaluate the diagnostic value of sDNA testing for multiple markers of colorectal cancer (CRC) and advanced adenoma.METHODS: The PubMed, Science Direct, Biosis Review, Cochrane Library and Embase databases were systematically searched in January 2012 without time restriction. Meta-analysis was performed using a random-effects model using sensitivity, specificity, diagnostic OR (DOR), summary ROC curves, area under the curve (AUC), and 95% CIs as effect measures. Heterogeneity was measured using the χ2test and Q statistic; subgroup analysis was also conducted.RESULTS: A total of 20 studies comprising 5876 individuals were eligible. There was no heterogeneity for CRC, but adenoma and advanced adenoma harboured considerable heterogeneity influenced by risk classification and various detection markers. Stratification analysis according to risk classification showed that multiple markers had a high DOR for the high-risk subgroups of both CRC (sensitivity 0.759 [95% CI 0.711 to 0.804]; specificity 0.883 [95% CI 0.846 to 0.913]; AUC 0.906) and advanced adenoma (sensitivity 0.683 [95% CI 0.584 to 0.771]; specificity 0.918 [95% CI 0.866 to 0.954]; AUC 0.946) but not for the average-risk subgroups of either. In the methylation subgroup, sDNA testing had significantly higher DOR for CRC (sensitivity 0.753 [95% CI 0.685 to 0.812]; specificity 0.913 [95% CI 0.860 to 0.950]; AUC 0.918) and advanced adenoma (sensitivity 0.623 [95% CI 0.527 to 0.712]; specificity 0.926 [95% CI 0.882 to 0.958]; AUC 0.910) compared with the mutation subgroup. There was no significant heterogeneity among studies for subgroup analysis.CONCLUSION: sDNA testing for multiple markers had strong diagnostic significance for CRC and advanced adenoma in high-risk subjects. Methylation makers had more diagnostic value than mutation markers.
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Chi, Xiao-Ling, Mei-Jie Shi, Huan-Ming Xiao, Yu-Bao Xie, and Gao-Shu Cai. "The Score Model Containing Chinese Medicine Syndrome Element of Blood Stasis Presented a Better Performance Compared to APRI and FIB-4 in Diagnosing Advanced Fibrosis in Patients with Chronic Hepatitis B." Evidence-Based Complementary and Alternative Medicine 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/3743427.
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This study aims to explore a useful noninvasive assessment containing TCM syndrome elements for liver fibrosis in CHB patients. The demographic, clinical, and pathological data were retrospectively collected from 709 CHB patients who had ALT less than 2 times the upper limit of normal from April 2009 to October 2012. Logistical regression and area under receiver-operator curve (AUROC) were used to determine the diagnostic performances of simple tests for advanced fibrosis (Scheuer stage, F ≥ 3). Results showed that the most common TCM syndrome element observed in this CHB population was dampness and Qi stagnation, followed by blood stasis, by heat, and less by Qi deficiency and Yin deficiency. The logistical regression analysis identified AST ≥ 35 IU/L, PLT ≤ 161 × 109/L, and TCM syndrome element of blood stasis as the independent risk factors for advanced fibrosis. Therefore, a score model containing these three factors was established and tested. The score model containing blood stasis resulted in a higher AUC (AUC = 0.936) compared with APRI (AUC = 0.731) and FIB-4 (AUC = 0.709). The study suggested that the score model containing TCM syndrome element of blood stasis could be used as a useful diagnostic tool for advanced fibrosis in CHB patients and presented a better performance compared to APRI and FIB-4.
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Siranec, Marek, Marek Höger, and Alena Otcenasova. "Advanced Power Line Diagnostics Using Point Cloud Data—Possible Applications and Limits." Remote Sensing 13, no. 10 (May 11, 2021): 1880. http://dx.doi.org/10.3390/rs13101880.
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The advance in remote sensing techniques, especially the development of LiDAR scanning systems, allowed the development of new methods for power line corridor surveys using a digital model of the powerline and its surroundings. The advanced diagnostic techniques based on the acquired conductor geometry recalculation to extreme operating and climatic conditions were proposed using this digital model. Although the recalculation process is relatively easy and straightforward, the uncertainties of input parameters used for the recalculation can significantly compromise such recalculation accuracy. This paper presents a systematic analysis of the accuracy of the recalculation affected by the inaccuracies of the conductor state equation input variables. The sensitivity of the recalculation to the inaccuracy of five basic input parameters was tested (initial temperature and mechanical tension, elasticity modulus, specific gravity load and tower span) by comparing the conductor sag values when input parameters were affected by a specific inaccuracy with an ideal sag-tension table. The presented tests clearly showed that the sag recalculation inaccuracy must be taken into account during the safety assessment process, as the sag deviation can, in some cases, reach values comparable to the minimal clearance distances specified in the technical standards.
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Gałązka-Sobotka, Małgorzata, Jakub Gierczyński, Jerzy Gryglewicz, Konrad Rejdak, Jarosław Sławek, Agnieszka Słowik, Monika Adamczyk-Sowa, Alina Kułakowska, Malina Wieczorek, and Halina Bartosik-Psujek. "The multiple sclerosis patient’s journey model in Poland – future tasks." Aktualności Neurologiczne 21, no. 2 (December 17, 2021): 76–85. http://dx.doi.org/10.15557/an.2021.0009.
Повний текст джерелаАнотація:
Improvement of the diagnostic and therapeutic processes and optimal use of resources in the context of health care system specificity accelerate the diagnosis and treatment onset, as well as improve the quality of life of patients with multiple sclerosis. International experience and data from clinical practice in Poland gave rise to a number of guidelines for the needed measures, from increasing the awareness about multiple sclerosis among the society and doctors in general, through expanding outpatient medical care, to proposing a model network of healthcare centres dedicated to patients with multiple sclerosis. It was pointed out that there is a need for a network of clinics specialised in the diagnosis and treatment of multiple sclerosis (MS clinics) and centres for comprehensive diagnosis and treatment, with a higher reference level and all the competences of an MS clinic, and, at the same time, providing both consultations in difficult clinical cases and access to the most advanced diagnostic and therapeutic methods. Attention was also drawn to the need to use modern e-health tools, which should improve the diagnostic and therapeutic process, as well as tighten the coordination of care by enabling an effective exchange of information between the patient and the entire interdisciplinary team involved in the therapeutic process.
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Čupera, Jiří, and Miroslav Havlíček. "Alternative methods of fuel consumption metering based on the on-board diagnostics outputs." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 53, no. 4 (2005): 23–32. http://dx.doi.org/10.11118/actaun200553040023.
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The article describes alternative methods of fuel consumption measurement based on model with using the diagnostic outputs of engine control unit. On-board diagnosis (the second level, known as OBD-2) has been mandated by government regulation because of advanced damage control systems in newer cars. However, its signals can be used for accurate analyses of power or torque measurement. On-board diagnostics offers many various parameters such a spark advance, intake air temperature, coolant temperature, throttle position, air flow mass and so on. Many of them have been unavailable without using sophisticated and expensive instrumentation. In the article are described two ways of fuel consumption measuring which are based on intake air consumption and knowledge about air-fuel ratio. First of them is founded on voltage output of oxygen sensor, the second on short (long) term fuel trim. As is shown at the end the second way gives more accurately results.
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Kumar, Satish, Paras Kumar, and Girish Kumar. "Degradation assessment of bearing based on machine learning classification matrix." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (March 27, 2021): 395–404. http://dx.doi.org/10.17531/ein.2021.2.20.
Повний текст джерелаАнотація:
In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.
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Kim, Su-Beom, Taegu Kim, and Daroh Lim. "A Study on A Demand Forecasting and Characterization of Diffusion Process for Advanced Diagnostic Imaging Equipment." Journal of Health Informatics and Statistics 47, no. 1 (February 28, 2022): 74–78. http://dx.doi.org/10.21032/jhis.2022.47.1.74.
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Objectives: In this study, we explore the demand forecasting of Advanced Diagnostic imaging Equipment.Methods: The analysis was based on logistic diffusion model. We analyze the specific pattern of each equipment’s diffusion curve by interpreting the parameter estimates of logistic diffusion model.Results: Our findings are follows. First, Computed tomography is in the stage of saturation and so, the future demands of that is not too large. Second, Magnetic resonance imaging (MRI) is expected that it will take about 5 years to reach saturation, and further growth is expected to continue. Third, Positron emission tomography (PET) has been shown to be saturated, and therefore, it is not expected that there will be a rapid increase in demand in the future. However, since demand data has been declining since 2000, it is said that additional data collection is required to reliably predict future demand.Conclusions: As a result of analyzing the demand for three major advanced diagnostic imaging equipment, it was found that the domestic market is generally in saturation. Therefore, a future research task will be to predict and analyze the demand for advanced diagnostic imaging equipment in consideration of the government’s policy changes.
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Poliński, Janusz. "Diagnostics of Track Infrastructure as Part of the Digitisation of Russian Railways." Problemy Kolejnictwa - Railway Reports 64, no. 188 (September 2020): 149–60. http://dx.doi.org/10.36137/1886e.
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Technical diagnostics is an integral part of the railway maintenance process. Through timely maintenance, in addition to ensuring the safety, functional and technical reliability of the infrastructure, maintenance costs are reduced and downtime losses, due to failures or premature repair requests, are eliminated or reduced. The track infrastructure diagnostic tools have evolved. This is related to, among others, the miniaturisation of instruments, reading accuracy during motion, as well as upgraded measurement automation and result analysis. Currently, data obtained from multifunctional diagnostic tools is the basis for the developed Russian railway infrastructure maintenance and operation digital model. The strategic development of mobile diagnostic labs is the gradual transition to solutions with advanced digital analysis, supported by artificial intelligence, monitoring and forecasting. The article presents the development of mobile labs for the railroad infrastructure condition diagnosis up to the current solutions, in which measurements take place without human intervention and the obtained information is transmitted in real time to the analysis and decision centres. Keywords: rail transport, measuring wagons, digitisation of railways, Russian railways
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Wu, Xiangyang, Haibin Shi, and Haiping Zhu. "Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks." Electronics 12, no. 3 (February 3, 2023): 768. http://dx.doi.org/10.3390/electronics12030768.
Повний текст джерелаАнотація:
Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature.
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Signorovitch, James, Zhou Zhou, Jason Ryan, and Anita Chawla. "Comprehensive genomic profiling (CGP) versus conventional molecular diagnostic testing of patients with advanced non-small cell lung cancer (NSCLC): Overall survival (OS) and cost in a U.S. health plan population." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 6599. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.6599.
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6599 Background: Molecular diagnostic testing options in NSCLC include conventional testing (specific alterations in single genes or multi-gene panels), and CGP (all classes of genomic alterations—base pair substitutions, copy number, insertions/deletions, and rearrangements in multi-gene panels). Guidelines recommend broad molecular profiling to enable genomic matching with available compendia-based and investigational treatment options. This study estimated the incremental benefits and costs of CGP versus conventional testing of patients with advanced NSCLC. Methods: The impacts of increased use of CGP (via FoundationOne) versus conventional molecular testing on OS and on a commercial US health plan budget were estimated using a decision-analytic model. The number of patients needed to test with CGP to add 1 life year was also estimated. Model inputs were based on published literature (incidence rates, OS associated with drugs indicated for advanced NSCLC), real-world data (testing rates, and biopsy, conventional testing, and medical service costs from administrative claims data analyses), list price of FoundationOne, and assumptions for clinical trial participation. Results: Among 2 million covered lives, an estimated 532 had advanced NSCLC and 266 received molecular diagnostic testing. An increase in CGP use from 2% to 10% (+21 patients receiving CGP) was associated with +2 years in population OS and a budget impact of $0.018 per member per month (PMPM). The budget impact was primarily attributable to changes in drug use, longer treatment, and longer survival (collectively $0.013 PMPM) with the remainder due to CGP cost ($0.005 PMPM). Approximately 11 patients need to be tested with CGP versus conventional molecular diagnostic testing to add 1 life year. Conclusions: An increase in molecular diagnostic testing with CGP versus conventional testing to inform treatment decisions in patients with advanced NSCLC was associated with a gain in OS and a modest health plan budget impact, with most of the added costs attributable to increased use of effective treatments and prolonged survival.
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Ehrhardt, J., and H. Handels. "Medical Image Computing for Computer-supported Diagnostics and Therapy." Methods of Information in Medicine 48, no. 01 (2009): 11–17. http://dx.doi.org/10.3414/me9131.
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Summary Objectives: Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. Methods: For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. Results: From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. Conclusions: The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient’s image data and have the future potential to improve medical diagnostics and patient treatment.
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Tikhonova, V. S., I. S. Gruzdev, E. V. Kondratyev, K. A. Mikhaylyuk та G. G. Kаrmаzаnovsky. "Texture analysis of contrast enhancement СT in the differential diagnosis of mass-forming pancreatitis and pancreatic ductal adenocarcinoma". Medical Visualization 26, № 1 (5 березня 2022): 140–54. http://dx.doi.org/10.24835/10.24835/1607-0763-1068.
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Purpose. Improving the efficiency of CT in the differential diagnosis of mass-forming pancreatitis (MFP) and pancreatic ductal adenocarcinoma (PDAC) making a diagnostic model based using a combination of texture features and contrast enhancement features.Methods and materials. 45 patients with histologically confirmed non-metastatic locally advanced PDAC and 13 patients with MFP where underwent CT examination with contrast enhancement. For each group, the ratio of the densities of intact pancreatic tissue and tumors, the relative tumor enhancement ratio (RTE) in all enhanced phases of CT, 94 texture features for each phase of the study were calculated and compared. The selection of predictors in the logistic model was carried out in 2 stages: 1) selection of predictors based on one-factor logistic models, the selection criterion was padj <0.2; 2) selection of predictors using LASSO-regression after standardization of variables. The selected predictors were included in a logistic regression model without interactions.Results. There were statistically significant differences in 14, 17, 4 out of 94 for the unenhanced, arterial, and venous phases of the study, respectively (p < 0.05). After selection, the final diagnostic model included the texture features CONVENTIONAL HUQ2 and DISCRETIZED HUQ1 for the unenhanced phase, DISCRETIZED HUQ1 and GLRLM RLNU for the arterial phase, DISCRETIZED Skewness for the venous phase, RTE for the delayed CT phase. The diagnostic model was built showed an accuracy of 81% in the diagnosis of MFP.Conclusion. We have developed a diagnostic model, including textural parameters and contrast enhancement features, which allows preoperatively distinguish MFP and PDAC, the developed model will increase the accuracy of preoperative diagnosis.
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Maciejczyk, Mateusz, Cezary Pawlukianiec, Małgorzata Żendzian-Piotrowska, Jerzy Robert Ładny, and Anna Zalewska. "Salivary Redox Biomarkers in Insulin Resistance: Preclinical Studies in an Animal Model." Oxidative Medicine and Cellular Longevity 2021 (September 9, 2021): 1–18. http://dx.doi.org/10.1155/2021/3734252.
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Insulin resistance (IR) is a condition of impaired tissue response to insulin. Although there are many methods to diagnose IR, new biomarkers are still being sought for early and noninvasive diagnosis of the disease. Of particular interest in laboratory diagnostics is saliva collected in a stress-free, noninvasive, and straightforward manner. The purpose of the study was to evaluate the diagnostic utility of salivary redox biomarkers in preclinical studies in an animal model. The study was conducted on 20 male Wistar rats divided into two equal groups: a standard diet and a high-fat diet (HFD). In all rats fed the HFD, IR was confirmed by an elevated homeostasis model assessment (HOMA-IR) index. We have shown that IR is responsible for the depletion of the enzymatic (↓superoxide dismutase) and nonenzymatic (↓ascorbic acid, ↓reduced glutathione (GSH)) antioxidant barrier at both the central (serum/plasma) and salivary gland (saliva) levels. In IR rats, we also demonstrated significantly higher concentrations of protein/lipid oxidation (↑protein carbonyls, ↑4-hydroxynoneal (4-HNE)), glycation (↑advanced glycation end products), and nitration (↑3-nitrotyrosine) products in both saliva and blood plasma. Salivary nonenzymatic antioxidants and oxidative stress products generally correlate with their blood levels, while GSH and 4-HNE have the highest correlation coefficient. Salivary GSH and 4-HNE correlate with body weight and BMI and indices of carbohydrate metabolism (glucose, insulin, HOMA-IR) and proinflammatory adipokines (leptin, resistin, TNF-α). These biomarkers differentiate IR from healthy controls with very high sensitivity (100%) and specificity (100%). The high diagnostic utility of salivary GSH and 4-HNE is also confirmed by multivariate regression analysis. Summarizing, saliva can be used to assess the systemic antioxidant status and the intensity of systemic oxidative stress. Salivary GSH and 4-HNE may be potential biomarkers of IR progression. There is a need for human clinical trials to evaluate the diagnostic utility of salivary redox biomarkers in IR conditions.
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Jovic, Dave, Jonathan Mulford, Kathryn Ogden, and Nadia Zalucki. "Diagnosis and management of chronic hip and knee pain in a Tasmanian orthopaedic clinic: a study assessing the diagnostic and treatment planning decisions of an advanced scope physiotherapist." Australian Journal of Primary Health 25, no. 1 (2019): 60. http://dx.doi.org/10.1071/py18076.
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The aim of this study is to investigate the clinical effectiveness of an advanced practice physiotherapist triaging patients referred from primary care to the orthopaedic clinic with chronic hip and knee pain. An exploratory study design was used to assess 87 consecutive patients referred from general practice in Northern Tasmania. Patients were assessed by both an advanced practice physiotherapist and a consultant orthopaedic surgeon. Diagnostic and treatment decisions were compared, with the orthopaedic consultant decision defined as the gold standard. By using these decisions, over and under referral rates to orthopaedics could be calculated, as well as the surgical conversion rate. Conservative care of patients referred to the orthopaedic clinic with hip and knee pain was limited. The diagnostic agreement between the advanced scope physiotherapist and the orthopaedic surgeon was almost perfect (weighted kappa 0.93 (95% CI 0.87–1.00)), with treatment agreement substantial (weighted kappa 0.75 (95% CI 0.62–0.89)). Under a physiotherapist-led triage service, the surgical conversion rate doubled from 38% to 78%. An advanced physiotherapist assessing and treating patients with chronic hip and knee pain made decisions that match substantially with decisions made by an orthopaedic consultant. A model of care utilising an advanced physiotherapist in this way has the potential to support high-quality orthopaedic care in regional centres.
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Dev, Ajay, and Sanjay Kumar Malik. "Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data." International Journal of E-Health and Medical Communications 12, no. 5 (September 2021): 67–83. http://dx.doi.org/10.4018/ijehmc.20210901.oa5.
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The healthcare domain gets wide attention among the research community due to incremental data growth, advanced diagnostic tools, medical imaging processes, and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is a tough task due to its nature. A large number of machine learning techniques are presented for handling the healthcare data and right diagnosis of disease. However, the accuracy is one of primary concerns regarding the disease diagnosis. Hence, this study explores the applicability of deep neural network (DNN) technique for handling the imbalance of healthcare data. An artificial bee colony technique is adopted to determine the relevant features of stroke disease called ABC-FS-optimized DNN. The performance of proposed ABC-FS-optimized DNN model is evaluated using accuracy, precision, and recall parameters and compared with state of art existing techniques. The simulation results showed that proposed model obtains 87.09%, 84.28%, and 85.72% accuracy, precision, and recall rates, respectively.
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Gradwohl, Christopher, Vesna Dimitrievska, Federico Pittino, Wolfgang Muehleisen, András Montvay, Franz Langmayr, and Thomas Kienberger. "A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic." Energies 14, no. 5 (February 25, 2021): 1261. http://dx.doi.org/10.3390/en14051261.
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Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.
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Diamond-Fox, Sadie. "Undertaking consultations and clinical assessments at advanced level." British Journal of Nursing 30, no. 4 (February 25, 2021): 238–43. http://dx.doi.org/10.12968/bjon.2021.30.4.238.
Повний текст джерелаАнотація:
Once deemed the reserve of doctors, ‘the medical interview’ has since transitioned across professional boundaries and is now a key part of the advanced clinical practitioner (ACP) role. Much of the literature surrounding this topic focuses on a purely medical model; however, the ACPs' use of consultation and clinical assessment of complex patient caseloads with undifferentiated and undiagnosed diseases is now a regular feature in healthcare practice. This article explores how knowledge of the fundamental principles surrounding ACP–patient communications, along with the use of appropriate consultation frameworks and examination skills, can provide a deeper insight and enhance the existing skills of the ACP. A comprehensive guide to undertaking patient consultations, physical examination and diagnostic reasoning on a body systems basis is explored in future issues of this Advanced Clinical Practice series.
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Glöckler, Martin, Andreas Koch, Julia Halbfaß, Verena Greim, Andrè Rüffer, Robert Cesnjevar, Stephan Achenbach, and Sven Dittrich. "Assessment of cavopulmonary connections by advanced imaging: value of flat-detector computed tomography." Cardiology in the Young 23, no. 1 (March 8, 2012): 18–26. http://dx.doi.org/10.1017/s104795111200025x.
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AbstractObjectivesTo investigate the impact of flat-detector computed tomography on the clinical assessment of patients with cavopulmonary connections, and to evaluate the obtained diagnostic accuracy and supplementary information, as well as the value of overlaid three-dimensional reconstructions on fluoroscopic images during catheter-based interventions.MethodsWe analysed 31 consecutive patients retrospectively in whom flat-detector computed tomography was used to visualise the cavopulmonary connection. We investigated patients with cavopulmonary connections either early post-operatively (first group), before converting to a total cavopulmonary connection (second group), and patients with failing total cavopulmonary connection (third group). Flat-detector computed tomography based on a single rotational angiography was used to create a three-dimensional vascular model. The clinical value of flat-detector computed tomography was evaluated using standard categories of diagnostic utility. Used contrast volume and radiation exposure were quantified.ResultsWithin 18 months, flat-detector computed tomography was performed in 31 cases with cavopulmonary connections. The median age was 1.9 years (range 0.3–43 years). In the first group, we found anomalies in 4 out of 8 cases, which led to therapeutic or prophylactic procedures; in the second and third groups, we performed interventions in 14 out of 23 cases. The overall clinical value was always rated superior to conventional biplane angiography. The median dose area product was 91.8 microgray square metres (range 33.0–679.3 microgray square metres). The required contrast medium was 2.08 millilitres per kilogram (range 0.66–4.7 millilitres per kilogram).ConclusionFlat-detector computed tomography improves the diagnostic accuracy in cavopulmonary connections and provides additional diagnostic information, which may lead to therapeutic or prophylactic procedures. Overlaid three-dimensional images on fluoroscopy facilitate and provide security for interventions.
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Asad, Bilal, Toomas Vaimann, Anouar Belahcen, Ants Kallaste, Anton Rassõlkin, and M. Naveed Iqbal. "The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics." Applied Sciences 10, no. 21 (October 27, 2020): 7572. http://dx.doi.org/10.3390/app10217572.
Повний текст джерелаАнотація:
This paper presents a hybrid finite element method (FEM)–analytical model of a three-phase squirrel cage induction motor solved using parallel processing for reducing the simulation time. The growing development in artificial intelligence (AI) techniques can lead towards more reliable diagnostic algorithms. The biggest challenge for AI techniques is that they need a big amount of data under various conditions to train them. These data are difficult to obtain from the industries because they contain low numbers of possible faulty cases, as well as from laboratories because a limited number of motors can be broken for testing purposes. The only feasible solution is mathematical models, which in the long run can become part of advanced diagnostic techniques. The benefits of analytical and FEM models for their speed and accuracy respectively can be exploited by making a hybrid model. Moreover, the concept of cloud computing can be utilized to reduce the simulation time of the FEM model. In this paper, a hybrid model being solved on multiple processors in a parallel fashion is presented. The results depict that by dividing the rotor steps among several processors working in parallel, the simulation time reduces considerably. The simulation results under healthy and broken rotor bar cases are compared with those taken from a laboratory setup for validation.
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Vogelgesang, Felicitas, Marc Dewey, and Peter Schlattmann. "The Evaluation of Bivariate Mixed Models in Meta-analyses of Diagnostic Accuracy Studies with SAS, Stata and R." Methods of Information in Medicine 57, no. 03 (May 2018): 111–19. http://dx.doi.org/10.3414/me17-01-0021.
Повний текст джерелаАнотація:
Summary Background: Meta-analyses require a thoroughly planned procedure to obtain unbiased overall estimates. From a statistical point of view not only model selection but also model implementation in the software affects the results. Objectives: The present simulation study investigates the accuracy of different implementations of general and generalized bivariate mixed models in SAS (using proc mixed, proc glimmix and proc nlmixed), Stata (using gllamm, xtmelogit and midas) and R (using reitsma from package mada and glmer from package lme4). Both models incorporate the relationship between sensitivity and specificity – the two outcomes of interest in meta-analyses of diagnostic accuracy studies – utilizing random effects. Methods: Model performance is compared in nine meta-analytic scenarios reflecting the combination of three sizes for meta-analyses (89, 30 and 10 studies) with three pairs of sensitivity/specificity values (97%/87%; 85%/75%; 90%/93%). Results: The evaluation of accuracy in terms of bias, standard error and mean squared error reveals that all implementations of the generalized bivariate model calculate sensitivity and specificity estimates with deviations less than two percentage points. proc mixed which together with reitsma implements the general bivariate mixed model proposed by Reitsma rather shows convergence problems. The random effect parameters are in general underestimated. Conclusions: This study shows that flexibility and simplicity of model specification together with convergence robustness should influence implementation recommendations, as the accuracy in terms of bias was acceptable in all implementations using the generalized approach.
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Huang, Sue-Fen. "Cognitive diagnostic assessment based on knowledge structure." MATEC Web of Conferences 169 (2018): 01020. http://dx.doi.org/10.1051/matecconf/201816901020.
Повний текст джерелаАнотація:
The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. In order to insight the misconception of learning basic mathematics and progress teaching. This method integrates Fuzzy Logic Model of Perception (FLMP) and Interpretive Structural Modelling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. In this paper, some well-known knowledge structure assessment methods will be discussed. For item connection, Bart et al ordering theory and Takeya’s item relational structure provided ordering coefficient to construct item relationships and hierarchies. For concepts or skills connection, Warfield’s ISM and Lin et al Concept Advanced Interpretive Structural Modelling (CAISM) provided to construct graphic relationship among elements and display the individualized concept hierarchy structure by numeric and picture. Samples contain 427 which come from Min-Hwei Junior College. Subjects were analyzed by CAISM. It shows the traditional assessment is not the only criteria; it must be combined with other assessment tools. The result shows that CAISM gives meaningful learning and lacks of learners.
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Curtis, P. "Industry applications of advanced flow diagnostics experience in the BAE Systems ground effects rig." Aeronautical Journal 109, no. 1093 (March 2005): 119–27. http://dx.doi.org/10.1017/s0001924000000622.
Повний текст джерелаАнотація:
Abstract This paper reviews some of the work performed on the ground effects rig at BAE Systems in Warton, the now unique facility for modelling dynamic vertical landings and measuring hot gas ingestion. The paper discusses the flow diagnostic tools which have been used in recent times, up to the complexity of 3D PIV, and uses examples from the F-35 JSF programme to illustrate these. The paper hopefully demonstrates the difficulties of using these tools, as well as the benefits they can bring to a development programme. The ground effects rig is a facility designed to measure temperature rise in aircraft intakes during vertical manoeuvres close to the ground. It is a complex facility that comes as close as possible to accurately modelling the flowfields around an aircraft moving both vertically and horizontally near the ground, with the ability to model dynamic pitch and roll at the same time. Standard instrumentation for the models consists of rapid response thermocouples mounted in a rake at the engine face. 45 thermocouples of 0·05mm diameter with a time constant of about 10ms are used. Although, with its standard instrumentation, the rig can measure how much hot gas gets to the engine face, it doesn’t show how it got there, or where it came from, which is the knowledge required to improve the design. Hence there is a need for flow diagnostics.
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Yang, Yuan, Ya Zheng, Hongling Zhang, Yandong Miao, Guozhi Wu, Lingshan Zhou, Haoying Wang, et al. "An Immune-Related Gene Panel for Preoperative Lymph Node Status Evaluation in Advanced Gastric Cancer." BioMed Research International 2020 (December 6, 2020): 1–7. http://dx.doi.org/10.1155/2020/8450656.
Повний текст джерелаАнотація:
Background and Aim: Gastric cancer (GC) is the common leading cause of cancer-related death worldwide. Immune-related genes (IRGs) may potentially predict lymph node metastasis (LNM). We aimed to develop a preoperative model to predict LNM based on these IRGs. Methods: In this paper, we compared and evaluated three machine learning models to predict LNM based on publicly available gene expression data from TCGA-STAD. The Pearson correlation coefficient (PCC) method was utilized to feature selection according to its relationships with LN status. The performance of the model was assessed using the area under the curve (AUC) and F1 score. Results: The Naive Bayesian model showed better performance and was constructed based on 26 selected gene features, with AUCs of 0.741 in the training set and 0.688 in the test set. The F1 score in the training set and test set was 0.652 and 0.597, respectively. Furthermore, Naive Bayesian model based on 26 IRGs is the first diagnostic tool for the identification of LNM in advanced GC. Conclusion: These results indicate that our new methods have the value of auxiliary diagnosis with promising clinical potential.
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Vidal, E., R. K. Yokomi, A. Moreno, E. Bertolini, and M. Cambra. "Calculation of Diagnostic Parameters of Advanced Serological and Molecular Tissue-Print Methods for Detection of Citrus tristeza virus: A Model for Other Plant Pathogens." Phytopathology® 102, no. 1 (January 2012): 114–21. http://dx.doi.org/10.1094/phyto-05-11-0139.
Повний текст джерелаАнотація:
Citrus tristeza virus (CTV) is one of the most important virus diseases that affect citrus. Control of CTV is achieved by grafting selected virus-free citrus scions onto CTV-tolerant or -resistant rootstocks. Quarantine and certification programs are essential for avoiding the entry and propagation of severe strains of CTV. Citrus nurseries in Spain and central California (United States) maintain zero-tolerance policies for CTV that require sensitive, specific, and reliable pathogen-detection methods. Tissue-print (TP) real-time reverse-transcriptase polymerase chain reaction (RT-PCR) assay was compared with the validated TP enzyme-linked immunosorbent assay (ELISA), using the CTV-specific monoclonal antibodies 3DF1 and 3CA5, for CTV detection. In total, 1,395 samples from healthy and CTV-infected nursery and mature tree plants were analyzed with both methods. The total agreement between both detection methods was substantial (Cohen's kappa index of 0.77 ± 0.03). The diagnostic parameters of each technique (i.e., the sensitivity, specificity, and likelihood ratios) were evaluated in a second test involving 658 Citrus macrophylla nursery plants. Mexican lime indexing was used to evaluate samples with discrepant results in the analysis. For TP-ELISA, a sensitivity of 0.8015, a specificity of 0.9963, and a positive and negative likelihood ratio of 216.42 and 0.199, respectively, were estimated. For TP real-time RT-PCR, a sensitivity of 0.9820, a specificity of 0.8519, and a positive and negative likelihood ratio of 6.63 and 0.021, respectively, were estimated. These diagnostic parameters show that TP real-time RT-PCR was the most sensitive technique, whereas TP-ELISA showed the highest specificity, validating the use of the molecular technique for routine CTV-detection purposes. In addition, our results show that the combination of both techniques can accurately substitute for the conventional biological Mexican lime index for the detection of CTV. The calculation of diagnostic parameters is discussed, as a necessary tool, to validate detection or diagnostic methods in plant pathology. Furthermore, assessment of the post-test probability of disease after a diagnostic result and CTV prevalence allows selection of the best method for accurate and reliable diagnosis.
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Fan, Jing, Jing Hong, Jun-Duo Hu, and Jin-Lian Chen. "Ion Chromatography Based Urine Amino Acid Profiling Applied for Diagnosis of Gastric Cancer." Gastroenterology Research and Practice 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/474907.
Повний текст джерелаАнотація:
Aim. Amino acid metabolism in cancer patients differs from that in healthy people. In the study, we performed urine-free amino acid profile of gastric cancer at different stages and health subjects to explore potential biomarkers for diagnosing or screening gastric cancer.Methods. Forty three urine samples were collected from inpatients and healthy adults who were divided into 4 groups. Healthy adults were in group A (n=15), early gastric cancer inpatients in group B (n=7), and advanced gastric cancer inpatients in group C (n=16); in addition, two healthy adults and three advanced gastric cancer inpatients were in group D (n=5) to test models. We performed urine amino acids profile of each group by applying ion chromatography (IC) technique and analyzed urine amino acids according to chromatogram of amino acids standard solution. The data we obtained were processed with statistical analysis. A diagnostic model was constructed to discriminate gastric cancer from healthy individuals and another diagnostic model for clinical staging by principal component analysis. Differentiation performance was validated by the area under the curve (AUC) of receiver-operating characteristic (ROC) curves.Results. The urine-free amino acid profile of gastric cancer patients changed to a certain degree compared with that of healthy adults. Compared with healthy adult group, the levels of valine, isoleucine, and leucine increased (P<0.05), but the levels of histidine and methionine decreased (P<0.05), and aspartate decreased significantly (P<0.01). The urine amino acid profile was also different between early and advanced gastric cancer groups. Compared with early gastric cancer, the levels of isoleucine and valine decreased in advanced gastric cancer (P<0.05). A diagnosis model constructed for gastric cancer with AUC value of 0.936 tested by group D showed that 4 samples could coincide with it. Another diagnosis model for clinical staging with an AUC value of 0.902 tested by 3 advanced gastric cancer inpatients of group D showed that all could coincide with the model.Conclusions. The noticeable differences of urine-free amino acid profiles between gastric cancer patients and healthy adults indicate that such amino acids as valine, isoleucine, leucine, methionine, histidine and aspartate are important metabolites in cell multiplication and gene expression during tumor growth and metastatic process. The study suggests that urine-free amino acid profiling is of potential value for screening or diagnosing gastric cancer.
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Degani, Rosanna. "Computerized Electrocardiogram Diagnosis: Fuzzy Approach." Methods of Information in Medicine 31, no. 04 (1992): 225–33. http://dx.doi.org/10.1055/s-0038-1634879.
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Abstract:This paper investigates the computerized analysis of electrocardiographic signals. The biological variability, the laáck of standards in the definition of measurements and of diagnostic criteria make the classification problem a complex task. Two basic methods of the diagnostic process are described: the statistical model and the deterministic approach. In particular, a model for ECG classification will be illustrated where the imprecise knowledge of the state of cardiac system and the vague definition of the pathological classes are taken care of by means of the fuzzy set formalism.
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Fabisiak, Luiza, and Karina Szczypor-Piasecka. "Diagnostic Analysis of Patients Qualified for Hip Replacement Using Multi-Criteria Methods." International Journal of Healthcare Information Systems and Informatics 15, no. 4 (October 2020): 56–69. http://dx.doi.org/10.4018/ijhisi.2020100104.
Повний текст джерелаАнотація:
Patients with advanced hip osteoarthritis are likely to suffer from biomechanical disorders. As many criteria inform how such patients are being qualified for alloplasty procedures, this article proposes a multi-criteria decisional framework in qualifying patients for treatment while undergoing diagnostic analysis for hip replacement surgery. In order to assess the patient's health condition, the competence of physicians and physiotherapists must first be checked. After creating the expert preference model and decision tree, the AHP method was applied followed by the Electre Tri method in the next stage of verification. Integrating these analytic procedures, a group of patients can be quickly evaluated and meaningfully profiled. Specifically, these patients can be classified in respect of their condition determined during hospitalisation as per the severity of degenerative disease and on the basis of their subjective feelings and diagnostics. The proposed methodology promises to allow optimal treatment to be assigned while enabling the appropriate classification and verification within group of patients targeted for hip replacement surgery.
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Luo, Qing-Tian, Qing Zhu, Xiao-Dan Zong, Ming-Kai Li, Hong-Sheng Yu, Chang-Yu Jiang, and Xiang Liao. "Diagnostic Performance of Transient Elastography Versus Two-Dimensional Shear Wave Elastography for Liver Fibrosis in Chronic Viral Hepatitis: Direct Comparison and a Meta-Analysis." BioMed Research International 2022 (September 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/1960244.
Повний текст джерелаАнотація:
Objective. This systematic review and meta-analysis aimed to compare the diagnostic performance of transient elastography (TE) and two-dimensional shear wave elastography (2D-SWE) for staging liver fibrosis in patients with chronic viral hepatitis (CVH). Methods. Pubmed, Embase, Web of Science, and Cochrane Library were searched (-01/08/2021) for studies comparing TE with 2D-SWE in patients with CVH. Other etiologies of chronic liver disease (CLD) and articles not published in SCI journals were excluded. The bivariate random-effects model was used to pool the performance of the TE and 2D-SWE. Results. Eight articles with a total of 1301 CVH patients were included. The prevalence of significant fibrosis ( fibrosis stage ≥ 2 ), advanced fibrosis ( fibrosis stage ≥ 3 ), and cirrhosis was 50.8%, 44.8%, and 34.7%, respectively. 2D-SWE expressed higher overall accuracy than TE in detecting significant fibrosis (0.93 vs. 0.85, P = 0.04 ). No significant difference among the overall diagnostic accuracy of TE and 2D-SWE in staging advanced fibrosis and cirrhosis was found. Conclusion. TE and 2D-SWE express good to excellent diagnostic accuracies to stage fibrosis in CVH patients. 2D-SWE compares favorably with TE especially for predicting significant fibrosis.
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Bittner, Alvah C. "A-Cadre: Advanced Family of Manikins for Workstation Design." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 38 (July 2000): 774–77. http://dx.doi.org/10.1177/154193120004403824.
Повний текст джерелаАнотація:
A-CADRE, an advanced cadre of 17 manikins, is delineated and shown to be valid for assuring population accommodation when applied to workstations. As with the earlier CADRE manikins (Bittner et al., 1986; 1987), a general four-stage procedure was followed in percentile description development, but with variations designed to assure greater reach accommodation within a tighter percentile boundary envelope. A-CADRE manikins were evaluated for process- and outcome-validity in the context of a redesign of a cockpit workstation using a diagnostic model (CAR-IV). A-CADRE's process utility was found both (1) virtually equivalent to a 400-member random sample and (2) slightly greater than the original CADRE. Outcome validity was virtually identical to the original CADRE, and “more than adequate for workstation design purposes.” A-CADRE can assure workstation population accommodation within a tighter percentile boundary.