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Статті в журналах з теми "Automative diagnosis"

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Автор: Grafiati

Опубліковано: 8 червня 2024

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1

Gaffet, Alexandre, Pauline Ribot, Elodie Chanthery, Nathalie Barbosa Roa, and Christophe Merle. "Data-Driven Capability-based Health Monitoring Method for Automative Manufacturing." PHM Society European Conference 6, no. 1 (June 29, 2021): 12. http://dx.doi.org/10.36001/phme.2021.v6i1.2811.

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Testing equipments are a crucial part of production quality control in the automotive industry. Their health needs to be controlled carefully to avoid quality issues and false alarms that reduce production efficiency, potentially leading to huge losses. The main challenge for this control is the large number of features leaning for automated reasoning. A data-based Health Monitoring System could be a solution. In manufacturing industries, a widely accepted index for evaluating process performance is the capability. It combines statistical measures for normal distributions in order to verify the ability of a process to produce an output within the specification limits. In this article we propose a capability-based prognosis and diagnosis method based on test data. Capability is calculated and compared to a known threshold. If the index value exceeds the threshold, then a diagnosis phase is initiated to find out which parts of the equipment are faulty. Data temporality is also taken into account. Data trends are used for prognosis.Test data are splited into periods. To respect the normality assumption of the capability, it is proposed to use a Gaussian Mixture Model (GMM) classification to extract all normal distributions found in one data period. Two approaches are discussed for selecting the number of clusters used for the classification. The first approach is based on the well-known Bayesian Information Criterion (BIC). The second approach uses a multi-criteria aggregation function learned by using machine learning on a synthetically gene-rated dataset. Some of the criteria used in the aggregation are inference based. Others are classical statistics extracted from the classes obtained by the GMM.For each of these classes the capability index is calculated and used for diagnosis and prognosis purposes. This method is applied on real data from In-Circuit Testing (ICT) machines for electronic components at a Vitesco factory in France.

2

Beyaz, Salih, Şahika Betül Yaylı, and Uğur Doktur. "Derin öğrenme ile otomatik kalça kırığı tanısı." TOTBİD Dergisi 22, no. 1 (January 1, 2022): 32–39. http://dx.doi.org/10.5578/totbid.dergisi.2022.07.

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3

Chen, Junying, Dongfang Li, Qingcai Chen, Wenxiu Zhou, and Xin Liu. "Diaformer: Automatic Diagnosis via Symptoms Sequence Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4432–40. http://dx.doi.org/10.1609/aaai.v36i4.20365.

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Automatic diagnosis has attracted increasing attention but remains challenging due to multi-step reasoning. Recent works usually address it by reinforcement learning methods. However, these methods show low efficiency and require task-specific reward functions. Considering the conversation between doctor and patient allows doctors to probe for symptoms and make diagnoses, the diagnosis process can be naturally seen as the generation of a sequence including symptoms and diagnoses. Inspired by this, we reformulate automatic diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but effective automatic Diagnosis model based on Transformer (Diaformer). We firstly design the symptom attention framework to learn the generation of symptom inquiry and the disease diagnosis. To alleviate the discrepancy between sequential generation and disorder of implicit symptoms, we further design three orderless training mechanisms. Experiments on three public datasets show that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5% with the highest training efficiency. Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.

4

Wang, Xiaoyu, Stephen D. J. McArthur, Scott M. Strachan, John D. Kirkwood, and Bruce Paisley. "A Data Analytic Approach to Automatic Fault Diagnosis and Prognosis for Distribution Automation." IEEE Transactions on Smart Grid 9, no. 6 (November 2018): 6265–73. http://dx.doi.org/10.1109/tsg.2017.2707107.

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5

Iliyasu, Abdullah M., Chastine Fatichah, and Khaled A. Abuhasel. "Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis." Automatika 57, no. 3 (January 2016): 822–35. http://dx.doi.org/10.7305/automatika.2016.10.1427.

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6

Mabrek, Abdelhakim, and Kamel E. Hemsas. "Induction motor inter-turn fault modeling and simulation using SSFR test for diagnosis purpose." Automatika 57, no. 4 (October 2016): 948–59. http://dx.doi.org/10.7305/automatika.2017.10.1805.

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7

Urrutia Iturritza, Miren, Phuthumani Mlotshwa, Jesper Gantelius, Tobias Alfvén, Edmund Loh, Jens Karlsson, Chris Hadjineophytou, et al. "An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings." Micromachines 15, no. 6 (May 28, 2024): 708. http://dx.doi.org/10.3390/mi15060708.

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Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

8

Mohammed, Mehmood Ali, Murtuza Ali Mohammed, and Vazeer Ali Mohammed. "Impact of Artificial Intelligence on the Automation of Digital Health System." International Journal of Software Engineering & Applications 13, no. 6 (November 30, 2022): 23–29. http://dx.doi.org/10.5121/ijsea.2022.13602.

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Automating digital systems in healthcare plays a significant role in transforming the quality-of-care services delivered to patients across the board. This role is anticipated to be accomplished by the development and implementation of artificial intelligence in healthcare which has the potential to impact the provision of healthcare services. This paper sought to investigate the impact of adopting and implementing artificial intelligence on the automation of digital health systems within the different levels of healthcare. The general objective of the research study was to investigate the impact of artificial intelligence in the automation of digital health systems. The specific goals were to understand the concept of artificial intelligence and how it automates digital strategies, to determine the AI systems that have been developed and implemented in the healthcare systems, to establish the factors that influence the adoption of AI in healthcare, and to find out the outcomes of implementing AI in digital health systems. The research employed the descriptive research design. The study population included healthcare workers, policymakers, IT specialists, and management teams in the healthcare sector in the State of Kentucky. The sampling technique for the study was the purposive sampling technique. The study collected data using semi-structured interviews administered through Google Teams and Zoom. Data analysis was analyzed using the computer-assisted software for analyzing qualitative data, NVivo. The findings were that AI as a technological concept has the potential to impact the automation of digital health systems and is key to automating health services such as the diagnosis and treatment of illnesses and management of claims and payments. The study recommended that policy supports the application of artificial intelligence in healthcare, thus enabling the automation of several healthcare services and thus improving the delivery of care.

9

Hoesterey, Steffen, and Linda Onnasch. "Operators over-rely even more when automated decision support is the exception and not the norm." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1070–74. http://dx.doi.org/10.1177/1071181322661502.

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Previous findings indicate that operator’s reliance towards a static automated aid increases with the degree of automation (DOA) especially when decision-making is affected. In this data reexamination of a previously conducted study, operators’ automation verification was investigated comparing a static automation supporting decision selection with an automation which in most trials only narrowed down possible diagnoses. Thus, in the majority of trials information sampling was essential for task completion in the latter condition. However, in a few trials the automation provided a diagnosis, too – giving participants the rare opportunity to fully rely on the automation. The question was investigated how participants behave in the exceptional occasions in which reliance is possible compared to participants who always have the opportunity to rely. Results show that when reliance was possible as an exception, participants verified their aid significantly less compared to the group who could rely throughout all trials. Implications for approaches of flexible automation are discussed.

10

Palla, Gabriella, Claudio Ughi, Graziano Cesaretti, Alessandro Ventura, and Giuseppe Maggiore. "“Automatic” diagnosis ofviral enteritis." Journal of Pediatrics 130, no. 6 (June 1997): 1013. http://dx.doi.org/10.1016/s0022-3476(97)70302-0.

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11

Masanov, D. V., and L. P. Sebina. "Automatic Diagnosis of Texturing." Fibre Chemistry 37, no. 2 (March 2005): 105–8. http://dx.doi.org/10.1007/s10692-005-0064-y.

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12

Witczak, Piotr. "Trends in Sensors Fault Diagnosis." Sensors 21, no. 6 (March 23, 2021): 2224. http://dx.doi.org/10.3390/s21062224.

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13

Brzdęk, Ewa, and Janusz Brzdęk. "The Warnke Method for the Diagnosis and Improvement of Phonological Competence in Special Needs Children." Education Sciences 10, no. 5 (April 29, 2020): 127. http://dx.doi.org/10.3390/educsci10050127.

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Speech, reading, and writing are the basic forms of linguistic communication. Therefore, it is very important to diagnose any problems with them as early and completely as possible, particularly in children with special needs. One of the methods that focuses primarily on the diagnosis and therapy of such learning difficulties is the one developed by Fred Warnke. The diagnostic solutions of the method were motivated by the following assumptions: (a) Automation of hearing, vision, and motor functions can be improved based on the level of brain activity; (b) the development and automation of phonological analysis and synthesis are based on cooperation between the two brain hemispheres. The main purpose of this paper is to present and discuss some research results that show the usefulness of diagnosis of the first stage of the Warnke method, as well as the training determined by it, in improving the phonological memory, language, and reading and writing skills of a group of four Polish children with special needs. The range of automation of each function was estimated on the basis of the values obtained in the diagnoses (initial and final). The final diagnosis showed an improvement in the levels of speech, reading, and writing. Thus, the research has confirmed that the Warnke method diagnosis may help to broaden and complement the standard evaluation methods of phonological competence for Polish children with special needs. The outcomes were so encouraging that we decided to present them to a wider audience.

14

Jeong, Yeonwoo, Yu-Jin Hong, and Jae-Ho Han. "Review of Machine Learning Applications Using Retinal Fundus Images." Diagnostics 12, no. 1 (January 6, 2022): 134. http://dx.doi.org/10.3390/diagnostics12010134.

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Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

15

Yi, Qu, Li Zhan-ming, and Li Er-chao. "Fault Detection and Diagnosis for Non-Gaussian Singular Stochastic Distribution Systems via Output PDFs." Automatika 53, no. 3 (January 2012): 236–43. http://dx.doi.org/10.7305/automatika.53-3.130.

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16

Kasperczuk, Anna, and Agnieszka Dardzinska. "Automatic system for IBD diagnosis." Procedia Computer Science 192 (2021): 2863–70. http://dx.doi.org/10.1016/j.procs.2021.09.057.

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17

Zhang, XueNong, and Wei Lu. "Automatic Diagnosis with Constraint Solver." International Journal of Information and Computer Science 4 (2015): 30. http://dx.doi.org/10.14355/ijics.2015.04.005.

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18

ABDUL-SADA, JAFAR W., and H. J. ABBAS. "Automatic diagnosis from electrocardiograms (ECGs)." International Journal of Systems Science 19, no. 11 (January 1988): 2157–62. http://dx.doi.org/10.1080/00207728808964108.

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19

Mossin, Eduardo André, Dennis Brandão, Guilherme Serpa Sestito, and Renato Veiga Torres. "Automatic Diagnosis for Profibus Networks." Journal of Control, Automation and Electrical Systems 27, no. 6 (July 25, 2016): 658–69. http://dx.doi.org/10.1007/s40313-016-0261-3.

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20

Nachlieli, Hila, Zachi Karni, and Shaul Raz. "Perception Guided Automatic Press Diagnosis." NIP & Digital Fabrication Conference 27, no. 1 (January 1, 2011): 784–87. http://dx.doi.org/10.2352/issn.2169-4451.2011.27.1.art00096_2.

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21

Koval, Svitlana, Svitlana Skrypnyk, and Inesa Shepel. "Conceptual principles of automation of accounting for work and salaries of employees of agricultural enterprises." University Economic Bulletin, no. 55 (December 29, 2022): 47–53. http://dx.doi.org/10.31470/2306-546x-2022-55-47-53.

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The subject of the study is the theoretical and practical aspects of automating labor and salary accounting in agricultural enterprises. The purpose of the article is to substantiate the conceptual foundations of automating the accounting of labor and wages of employees of agricultural enterprises, to identify differences in its directions and to evaluate competitive advantages and results for the needs and improvement of management. The methodological basis of the article was dialectical, historical, monographic, system-structural analysis and synthesis; economic comparisons; special methods of accounting. Results of the article. It was determined that the automation of accounting for labor and wages of employees of agricultural enterprises provides them with significant competitive advantages of development and management efficiency due to simplification, acceleration, objectivity and high quality of accounting; significant cost savings for its implementation; availability for management and owners. At the same time, three conceptual approaches to automation with corresponding software products, spheres and tools of their influence, and expected results are substantiated. This is the automation of the accounting of typical calculations and payments with integration into the general information system of the enterprise; automation of the accountant's workplace; combined with a combination of the first two. Their comparison is made and advantages and disadvantages, perspectives of use are shown. Field of application of results. The obtained results can be used in the work of accounting offices of enterprises, in the scientific and educational process of agrarian universities. Conclusions. In order to increase the efficiency of managing the development of agricultural enterprises on the basis of the automation of labor accounting and employee salaries, it is important to implement the latest software products for its support. In particular, we may be talking about a more improved version of the program "1C: Salary and Personnel Management 8.3". This version is very productive for both accounting specialists and PH-management of enterprises, because it provides: definition, assessment, diagnosis, forecasting and planning of personnel needs; management of attestation, training and financial motivation of employees; effective planning of employment of employees from an overview on the seasonality of agricultural work; personnel record keeping and personnel analysis; maintaining staffing in agricultural enterprises. Another large set of advantages concerns the automation of calculations provided by the named advanced version of the program. We are talking about the automation of employee salary calculations; taxes regulated by legislation, withholdings from wages and accruals to the wage fund; accruals and deductions according to any algorithms; display of accrued wages and taxes as part of enterprise expenses. It is also management of cash settlements with employees, including depositing; formation of settlement sheets of any kind; formation of payment information with the arrangement of information according to various criteria by dividing it by categories of employees, types of work, divisions and other features; calculation of sick leaves, vacations, premiums, bonuses and incentives.

22

Ramamurthi, K., and C. L. Hough. "Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations." Journal of Engineering for Industry 115, no. 3 (August 1, 1993): 268–77. http://dx.doi.org/10.1115/1.2901660.

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Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.

23

Sacha, J. P., K. J. Cios, and L. S. Goodenday. "Issues in automating cardiac SPECT diagnosis." IEEE Engineering in Medicine and Biology Magazine 19, no. 4 (2000): 78–88. http://dx.doi.org/10.1109/51.853485.

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24

Stiegler, Bernard, and Daniel Ross. "Elements for a Neganthropology of Automatic Man." Philosophy Today 65, no. 2 (2021): 241–64. http://dx.doi.org/10.5840/philtoday2021414397.

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Ours is an age of general automation. The factory that produced proletarians now extends to the biosphere; consequently, disautomatization is needed, which is the real meaning of autonomy. Autonomy and automatism must be reconceived as a composition rather than an opposition. Knowledge depends on hypomnesic automatisms that open up the possibility of what Socrates called “thinking for oneself”; digitalization thus requires a new epistemology that entails questions of political and libidinal economy. Today, automatization serves the autonomization of technics more than noetic autonomy, but reconceiving the latter necessarily involves reconsidering the meaning and character of desire. Greta Thunberg’s diagnosis of the irresponsibility of the generation before hers exposes the fact that the knowledge necessary to combat the consequences of contemporary technological development has been destroyed. The only possible counter to this irresponsibility lies in the reconstitution of knowledge, understood as the means by which humans struggle against entropy and anthropy.

25

Zhang, Fuxi, Guoming Sang, Zhi Liu, Hongfei Lin, and Yijia Zhang. "A doctor’s diagnosis experience enhanced transformer model for automatic diagnosis." Engineering Applications of Artificial Intelligence 134 (August 2024): 108675. http://dx.doi.org/10.1016/j.engappai.2024.108675.

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26

Kojima, Tomoyuki, and Masahiro Kato. "Algorithm for Automatic Diagnosis of Aphasia." Higher Brain Function Research 16, no. 3 (1996): 221–26. http://dx.doi.org/10.2496/apr.16.221.

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27

NAKAYAMA, Keizo. "Trends in automatic cytological diagnosis screening in Japan - Evaluation of automatic cytological diagnosis screening supporting AutoPap." Journal of the Japanese Society of Clinical Cytology 40, no. 2 (2001): 204–10. http://dx.doi.org/10.5795/jjscc.40.204.

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28

Sanchez-Sanchez, Paola A., José Rafael García-González, and Juan Manuel Rúa Ascar. "Automatic migraine classification using artificial neural networks." F1000Research 9 (June 16, 2020): 618. http://dx.doi.org/10.12688/f1000research.23181.1.

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Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

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Sanchez-Sanchez, Paola A., José Rafael García-González, and Juan Manuel Rúa Ascar. "Automatic migraine classification using artificial neural networks." F1000Research 9 (July 17, 2020): 618. http://dx.doi.org/10.12688/f1000research.23181.2.

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Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

30

Harbi, Zainab. "Automatic Diagnosis of Ovarian Cancer Based on Relative Entropy and Neural Network." Wasit Journal for Pure sciences 2, no. 3 (September 30, 2023): 108–18. http://dx.doi.org/10.31185/wjps.172.

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Ovarian Cancer is one of the most common causes of death for women in developing countries. Screening and early diagnoses of OC are urgently needed. Early diagnosis would help in consequence procedures and treatment. Mass spectrometry (MS) data is been used as an effective component of cancer diagnosis tools. However, these valuable data have a large number of dimensions that can affect the learning process in addition to time-consuming considerations. Feature selection plays an important role in reducing information redundancy, and deals with the invalidation that occurs in basic classification algorithms when there are too many features and huge datasets. To improve the automatic system diagnosis accuracy, entropy-based selection features are proposed. These features are combined with the novel learning capabilities of neural networks to achieve higher diagnostic accuracy. Experiments have been performed using different feature selection algorithms and machine learning classification approaches. Experimental results have proved that the proposed system performs better based on the measure of accuracy.

31

Lui, Sheng Jie, Cheng Xiang, and Shonali Krishnaswamy. "KAMEL: Knowledge Aware Medical Entity Linkage to Automate Health Insurance Claims Processing." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 22797–805. http://dx.doi.org/10.1609/aaai.v38i21.30314.

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Automating the processing of health insurance claims to achieve "Straight-Through Processing" is one of the holy grails that all insurance companies aim to achieve. One of the major impediments to this automation is the difficulty in establishing the relationship between the underwriting exclusions that a policy has and the incoming claim's diagnosis information. Typically, policy underwriting exclusions are captured in free-text such as "Respiratory illnesses are excluded due to a pre-existing asthma condition". A medical claim coming from a hospital would have the diagnosis represented using the International Classification of Disease (ICD) codes from the World Health Organization. The complex and labour-intensive task of establishing the relationship between free-text underwriting exclusions in health insurance policies and medical diagnosis codes from health insurance claims is critical towards determining if a claim should be rejected due to underwriting exclusions. In this work, we present a novel framework that leverages both explicit and implicit domain knowledge present in medical ontologies and pre-trained language models respectively, to effectively establish the relationship between free-text describing medical conditions present in underwriting exclusions and the ICD-10CM diagnosis codes in health insurance claims. Termed KAMEL (Knowledge Aware Medical Entity Linkage), our proposed framework addresses the limitations faced by prior approaches when evaluated on real-world health insurance claims data. Our proposed framework have been deployed in several multi-national health insurance providers to automate their health insurance claims.

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Sheketa, Vasyl, Mykola Pasieka, Svitlana Chupakhina, Nadiia Pasieka, Uliana Ketsyk-Zinchenko, Yulia Romanyshyn, and Olha Yanyshyn. "Information System for Screening and Automation of Document Management in Oncological Clinics." Open Bioinformatics Journal 14, no. 1 (November 19, 2021): 39–50. http://dx.doi.org/10.2174/1875036202114010039.

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Introduction: Automation of business documentation workflow in medical practice substantially accelerates and improves the process and results in better service development. Methods: Efficient use of databases, data banks, and document-oriented storage (warehouses data), including dual-purpose databases, enables performing specific actions, such as adding records, introducing changes into them, performing an either ordinary or analytical search of data, as well as their efficient processing. With the focus on achieving interaction between the distributed and heterogeneous applications and the devices belonging to the independent organizations, the specialized medical client application has been developed, as a result of which the quantity and quality of information streams of data, which can be essential for effective treatment of patients with breast cancer, have increased. Results: The application has been developed, allowing automating the management of patient records, taking into account the needs of medical staff, especially in managing patients’ appointments and creating patient’s medical records in accordance with the international standards currently in force. This work is the basis for the smoother integration of medical records and genomics data to achieve better prevention, diagnosis, prediction, and treatment of breast cancer (oncology). Conclusion: Since relevant standards upgrade the functioning of health care information technology and the quality and safety of patient’s care, we have accomplished the global architectural scheme of the specific medical automation system through harmonizing the medical services specified by the HL7 international.

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Yokoi, H., S. Fujita, K. Takabayashi, and T. Suzuki. "Automatic DPC Code Selection from Electronic Medical Records." Methods of Information in Medicine 47, no. 06 (2008): 541–48. http://dx.doi.org/10.3414/me9128.

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Summary Objectives: We extracted index terms related to diseases recorded in hospital discharge summaries and examined the capability of the vector space model to select a suitable diagnosis with these terms. Methods: By morphological analysis, we extracted index terms and constructed an original dictionary for the discharge summary analysis. We chose 125 different DPC (Japanese DRG system) codes for the diseases, each of which had more than 20 cases. We divided them into two groups. One group consisted of 5927 cases from 2004 fiscal year and was used to generate the document vector space according to the DPC. The other group of 3187 cases was collected to verify the automatic DPC selection by using data from 2005 fiscal year. The top 200 extracted index terms for each disease were used to calculate the weight of each disease. Results: The DPC code obtained by the calculated similarity was compared with the original codes of patients for 125 DPCs of 3187 cases. Eighty percent of the cases matched the diagnosis of the DPC (first six digits) and 56% of the cases completely matched all 14 digits of the DPC. Conclusions: We demonstrated that we could extract suitable terms for each disease and obtain characteristics, such as the diagnosis, from the calculated vectors. This technique can be used to measure the qualification of discharge summaries and to integrate discharge summaries among different facilities. By the text mining technique, we can characterize the contents of electronic discharge summaries and deduce diagnoses with the data.

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Aakre, Christopher, Mikhail Dziadzko, Mark Keegan, and Vitaly Herasevich. "Automating Clinical Score Calculation within the Electronic Health Record." Applied Clinical Informatics 08, no. 02 (April 2017): 369–80. http://dx.doi.org/10.4338/aci-2016-09-ra-0149.

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Summary Objectives: Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records. Methods: We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings. Results: We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable. Conclusion: Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments – partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study. Citation: Aakre C, Dziadzko M, Keegan MT, Herasevich V. Automating clinical score calculation within the electronic health record: A feasibility assessment. Appl Clin Inform 2017; 8: 369–380 https://doi.org/10.4338/ACI-2016-09-RA-0149

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Bystrova, I. V., and B. P. Podkopaev. "Functional Diagnosis of Digital State Automation Networks." Journal of the Russian Universities. Radioelectronics, no. 2 (June 5, 2018): 12–19. http://dx.doi.org/10.32603/1993-8985-2018-21-2-12-19.

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The problem of functional diagnosis of digital devices forming a network of state automata is considered. This task for the network components is supposed to be solved, and the corresponding diagnostic devices for them are provided. The possibility of their population transformation into the tools for the entire network diagnostics is shown. The result of the transformation with the restrictions on the number of components with errors is simplified in compare with the original population. A procedure is proposed allowing to find analytical expressions defining an auxiliary control and an error discriminator for the most probable case of error localization (all errors are concentrated in a certain component of the network). The first part of the article provides a solution of the functional diagnosis problem for the case when the parity functions of all components are scalar, and the class of detectable errors is given by unit multiplicity. Next, the result generalizes for the case of vector functions, where multiple errors can occur. The procedure minimizes the sought for functional diagnosis devices with respect to the order when preserving the initial detecting ability within any network component. The obtained results are illustrated by an example of construction of functional diagnosis equipment for ranging signal processing device in broadband short-range radio engineering navigation system.

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Aggarwal, Aditya, Siddhartha Gairola, Uddeshya Upadhyay, Akshay P. Vasishta, Diwakar Rao, Aditya Goyal, Kaushik Murali, Nipun Kwatra, and Mohit Jain. "Towards Automating Retinoscopy for Refractive Error Diagnosis." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 3 (September 6, 2022): 1–26. http://dx.doi.org/10.1145/3550283.

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Refractive error is the most common eye disorder and is the key cause behind correctable visual impairment, responsible for nearly 80% of the visual impairment in the US. Refractive error can be diagnosed using multiple methods, including subjective refraction, retinoscopy, and autorefractors. Although subjective refraction is the gold standard, it requires cooperation from the patient and hence is not suitable for infants, young children, and developmentally delayed adults. Retinoscopy is an objective refraction method that does not require any input from the patient. However, retinoscopy requires a lens kit and a trained examiner, which limits its use for mass screening. In this work, we automate retinoscopy by attaching a smartphone to a retinoscope and recording retinoscopic videos with the patient wearing a custom pair of paper frames. We develop a video processing pipeline that takes retinoscopic videos as input and estimates the net refractive error based on our proposed extension of the retinoscopy mathematical model. Our system alleviates the need for a lens kit and can be performed by an untrained examiner. In a clinical trial with 185 eyes, we achieved a sensitivity of 91.0% and specificity of 74.0% on refractive error diagnosis. Moreover, the mean absolute error of our approach was 0.75±0.67D on net refractive error estimation compared to subjective refraction measurements. Our results indicate that our approach has the potential to be used as a retinoscopy-based refractive error screening tool in real-world medical settings.

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Chirkov, Andrey, Larisa Gagarina, Nikolay Mironov, and Roman Lipovy. "Diagnosis automation methods in the subject area." Automation and modeling in design and management 2022, no. 4 (December 21, 2022): 37–45. http://dx.doi.org/10.30987/2658-6436-2022-4-37-45.

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This article gives an overview of some existing diagnostic automation methods applicable to a variety of subject areas. At present, many branches of industry, medicine, agriculture and agrotechnical economies are moving towards reducing the need for involving human resources in the processes of diagnosing equipment malfunctions, various diseases of both people and plants. The number of different methods for diagnostics and processing the received data increases over time, as do the data flow itself and the requirements for the processing accuracy and speed. An important task is to build adequate models for data analysis, taking into account random perturbations and the need for rapid research at the rate of incoming data. The only way to choose the most optimal method is to conduct a comparative analysis and correlate many factors to be considered when choosing a particular method

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Li, Jiawei, Zhidong Zhang, Xiaolong Zhu, Yunlong Zhao, Yuhang Ma, Junbin Zang, Bo Li, Xiyuan Cao, and Chenyang Xue. "Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks." Micromachines 13, no. 4 (March 24, 2022): 501. http://dx.doi.org/10.3390/mi13040501.

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Tongue diagnosis is an important part of the diagnostic process in traditional Chinese medicine (TCM). It primarily relies on the expertise and experience of TCM practitioners in identifying tongue features, which are subjective and unstable. We proposed a tongue feature classification framework based on convolutional neural networks to reduce the differences in diagnoses among TCM practitioners. Initially, we used our self-designed instrument to capture 482 tongue photos and created 11 data sets based on different features. Then, the tongue segmentation task was completed using an upgraded facial landmark detection method and UNET. Finally, we used ResNet34 as the backbone to extract features from the tongue photos and classify them. Experimental results show that our framework has excellent results with an overall accuracy of over 86 percent and is particularly sensitive to the corresponding feature regions, and thus it could assist TCM practitioners in making more accurate diagnoses.

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Havryliuk, V. I., and V. Yu Dub. "DIAGNOSIS OF RELAY-CONTACT DEVICES OF RAILWAY AUTOMATICS." Science and Transport Progress, no. 12 (September 25, 2006): 7–11. http://dx.doi.org/10.15802/stp2006/18451.

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The analysis of existing methods of construction of diagnostic tests of discrete devices is carried out, algorithms and the program of construction of checking and diagnostic tests for the relay-contact equipment of railway automatics are developed.

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Yan Huan, Ch’ng, Mohd Azam Osman, and Jong Hui Ying. "An Innovation-Driven Approach to Specific Language Impairment Diagnosis." Malaysian Journal of Medical Sciences 28, no. 2 (April 21, 2021): 161–70. http://dx.doi.org/10.21315/mjms2021.28.2.15.

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Background: Specific language impairment (SLI) diagnosis is inconvenient due to manual procedures and hardware cost. Computer-aided SLI diagnosis has been proposed to counter these inconveniences. This study focuses on evaluating the feasibility of computer systems used to diagnose SLI. Methods: The accuracy of Webgazer.js for software-based gaze tracking is tested under different lighting conditions. Predefined time delays of a prototype diagnosis task automation script are contrasted against with manual delays based on human time estimation to understand how automation influences diagnosis accuracy. SLI diagnosis binary classifier was built and tested based on randomised parameters. The obtained results were cross-compared to Singlims_ES.exe for equality. Results: Webgazer.js achieved an average accuracy of 88.755% under global lighting conditions, 61.379% under low lighting conditions and 52.7% under face-focused lighting conditions. The diagnosis task automation script found to execute with actual time delays with a deviation percentage no more than 0.04%, while manually executing time delays based on human time estimation resulted in a deviation percentage of not more than 3.37%. One-tailed test probability value produced by both the newly built classifier and Singlims_ES were observed to be similar up to three decimal places. Conclusion: The results obtained should serve as a foundation for further evaluation of computer tools to help speech language pathologists diagnose SLI.

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Liu, Yan, Jing Lei, Shasha Zeng, Yajie Wang, and Zhaohua Nian. "Design of satellite ground fault diagnosis system based on rule base." ITM Web of Conferences 47 (2022): 01013. http://dx.doi.org/10.1051/itmconf/20224701013.

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The automation and intelligentization of fault diagnosis system for satellite ground system directly affect the success of tasks and the reliability of the system. This paper designed a fault diagnosis system based on rule base, which contained satellite ground system failure rule base, failure model, abnormal and alarm mechanism. Software implementation has been verified by actual project, it shows that the fault diagnosis system based on rule base can improve the capacity of fault management functions, real-time monitoring and automatic fault diagnosis support system. In addition, fault analysis and location can enhance the automation level and efficiency of satellite fault diagnosis, make efficient and reliable diagnosis of remote sensing satellite receiving system, raise the success rate of satellite data receiving, and have good practicability and popularization.

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Martinho, Rita, Jéssica Lopes, Diogo Jorge, Luís Caldas de Oliveira, Carlos Henriques, and Paulo Peças. "IoT Based Automatic Diagnosis for Continuous Improvement." Sustainability 14, no. 15 (August 6, 2022): 9687. http://dx.doi.org/10.3390/su14159687.

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This work responds to the gap in integrating the Internet-of-Things in Continuous Improvement processes, especially to facilitate diagnosis and problem-solving activities regarding manufacturing workstations. An innovative approach, named Automatic Detailed Diagnosis (ADD), is proposed: a non-intrusive, easy-to-install and use, low-cost and flexible system based on industrial Internet-of-Things platforms and devices. The ADD requirements and architecture were systematized from the Continuous Improvement knowledge field, and with the help of Lean Manufacturing professionals. The developed ADD concept is composed of a network of low-power devices with a variety of sensors. Colored light and vibration sensors are used to monitor equipment status, and Bluetooth low-energy and time-of-flight sensors monitor operators’ movements and tasks. A cloud-based platform receives and stores the collected data. That information is retrieved by an application that builds a detailed report on operator–machine interaction. The ADD prototype was tested in a case study carried out in a mold-making company. The ADD was able to detect time performance with an accuracy between 89% and 96%, involving uptime, micro-stops, and setups. In addition, these states were correlated with the operators’ movements and actions.

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Avanzato, Roberta, and Francesco Beritelli. "Automatic ECG Diagnosis Using Convolutional Neural Network." Electronics 9, no. 6 (June 8, 2020): 951. http://dx.doi.org/10.3390/electronics9060951.

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Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases.

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Sun, Qiao, Ping Chen, Dajun Zhang, and Fengfeng Xi. "Pattern Recognition for Automatic Machinery Fault Diagnosis." Journal of Vibration and Acoustics 126, no. 2 (April 1, 2004): 307–16. http://dx.doi.org/10.1115/1.1687391.

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We present a generic methodology for machinery fault diagnosis through pattern recognition techniques. The proposed method has the advantage of dealing with complicated signatures, such as those present in the vibration signals of rolling element bearings with and without defects. The signature varies with the location and severity of bearing defects, load and speed of the shaft, and different bearing housing structures. More specifically, the proposed technique contains effective feature extraction, good learning ability, reliable feature fusion, and a simple classification algorithm. Examples with experimental testing data were used to illustrate the idea and effectiveness of the proposed method.

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Nunes, Luciano Comin, Placido Rogerio Pinheiro, Mirian Caliope Dantas Pinheiro, Marum Simao Filho, Rafael Espindola Comin Nunes, and Pedro Gabriel Caliope Dantas Pinheiro. "Automatic Detection and Diagnosis of Neurologic Diseases." IEEE Access 7 (2019): 29924–41. http://dx.doi.org/10.1109/access.2019.2899216.

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Sharan, Roneel V., Udantha R. Abeyratne, Vinayak R. Swarnkar, and Paul Porter. "Automatic Croup Diagnosis Using Cough Sound Recognition." IEEE Transactions on Biomedical Engineering 66, no. 2 (February 2019): 485–95. http://dx.doi.org/10.1109/tbme.2018.2849502.

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47

Liu, Jiang, Zhuo Zhang, Damon Wing Kee Wong, Yanwu Xu, Fengshou Yin, Jun Cheng, Ngan Meng Tan, et al. "Automatic glaucoma diagnosis through medical imaging informatics." Journal of the American Medical Informatics Association 20, no. 6 (November 2013): 1021–27. http://dx.doi.org/10.1136/amiajnl-2012-001336.

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VINK, J. P., M. LAUBSCHER, R. VLUTTERS, K. SILAMUT, R. J. MAUDE, M. U. HASAN, and G. DE HAAN. "An automatic vision-based malaria diagnosis system." Journal of Microscopy 250, no. 3 (April 2, 2013): 166–78. http://dx.doi.org/10.1111/jmi.12032.

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Fernandez-Granero, M. A., A. Sarmiento, D. Sanchez-Morillo, S. Jiménez, P. Alemany, and I. Fondón. "Automatic CDR Estimation for Early Glaucoma Diagnosis." Journal of Healthcare Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5953621.

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Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.

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Vinson, J. M., S. D. Grantham, and L. H. Ungar. "Automatic rebuilding of qualitative models for diagnosis." IEEE Expert 7, no. 4 (August 1992): 23–30. http://dx.doi.org/10.1109/64.153461.

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