Relevant bibliographies by topics / Medical Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Medical Prediction'

Author: Grafiati
Published: 29 March 2025

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Journal articles on the topic "Medical Prediction"

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Sabarinath U S and Ashly Mathew. "Medical Insurance Cost Prediction." Indian Journal of Data Communication and Networking 4, no. 4 (June 30, 2024): 1–4. http://dx.doi.org/10.54105/ijdcn.d5037.04040624.

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This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. To predict things that have never been so easy. In this project used to predict values that wonder how Insurance amount is normally charged. This is a medical insurance cost prediction model that uses a linear regression algorithm to predict the medical insurance charges of a person based on the given data. This project on predicting medical insurance costs can serve various purposes and address several needs that are Accurate Pricing Insurance companies need accurate predictions of medical insurance costs to set appropriate premiums for policyholders. Predictive models can analyse historical data and various factors such as age, gender, pre-existing conditions, lifestyle habits, and geographic location to estimate future healthcare expenses accurately. This Prediction model achieves three regression methods accuracy that the linear regression gets an accuracy of 74.45 %, whereas Ridge regression and Support Vector Regression gets 82.59% word-level state-of-the-art accuracy. The Medical Insurance Cost Prediction project, proposes a comprehensive approach to predict the medical cost, aiming to develop a robust and accurate system capable of predicting the accurate cost for a particular individual. Leveraging linear regression, our proposed system builds upon the successes of existing models like different types of regressions like linear regression, Ridge regression and Support Vector regression. We will put the Regression algorithm into practice and evaluate how it performs in comparison to the other three algorithms. By comparing the performance of these three methodologies, this project aims to identify the most effective approach for medical insurance cost prediction. Through rigorous evaluation and validation processes, the selected model will provide valuable insights for insurance companies, policymakers, and individuals seeking to optimize healthcare resource allocation and financial planning strategies.
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Liu, Enwu, Ryan Yan Liu, and Karen Lim. "Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events." Applied Sciences 13, no. 24 (December 6, 2023): 13041. http://dx.doi.org/10.3390/app132413041.

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Clinical prediction models are commonly utilized in clinical practice to screen high-risk patients. This enables healthcare professionals to initiate interventions aimed at delaying or preventing adverse medical events. Nevertheless, the majority of these models focus on calculating probabilities or risk scores for medical events. This information can pose challenges for patients to comprehend, potentially causing delays in their treatment decision-making process. Our paper presents a statistical methodology and protocol for the utilization of a Weibull accelerated failure time (AFT) model in predicting the time until a health-related event occurs. While this prediction technique is widely employed in engineering reliability studies, it is rarely applied to medical predictions, particularly in the context of predicting survival time. Furthermore, we offer a practical demonstration of the implementation of this prediction method using a publicly available dataset.
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Ramesh, Banoth, G. Srinivas, P. Ram Praneeth Reddy, M. D. Huraib Rasool, Divya Rawat, and Madhulita Sundaray. "Feasible Prediction of Multiple Diseases using Machine Learning." E3S Web of Conferences 430 (2023): 01051. http://dx.doi.org/10.1051/e3sconf/202343001051.

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Automated Multiple Disease Prediction System using Machine Learning is an advanced healthcare application that utilizes machine learning algorithms to accurately predict the likelihood of a patient having multiple diseases based on their medical history and symptoms. The system employs a comprehensive dataset of medical records and symptoms of various diseases, which are then analysed using machine learning techniques such as decision trees, support vector machines, and random forests. The system’s predictions are highly accurate, and it can assist medical professionals in making more informed decisions and providing better treatment plans for patients. Ultimately, the viable Multiple Disease Prediction System using Machine Learning has the potential to improve healthcare outcomes and reduce healthcare costs by predicting and preventing disease early.
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Kannan, S., G. Premalatha, M. Jamuna Rani, D. Jayakumar, P. Senthil, S. Palanivelrajan, S. Devi, and Kibebe Sahile. "Effective Evaluation of Medical Images Using Artificial Intelligence Techniques." Computational Intelligence and Neuroscience 2022 (August 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/8419308.

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This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson’s disease, the affected patients with Parkinson’s disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions.
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P., Renukadevi. "Lossless Medical Image Compression by Multi Oriented Prediction Technique." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1277–96. http://dx.doi.org/10.37200/ijpr/v24i5/pr201800.

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Prof. M. S. Patil, Kulkarni Sanika, and Khurpe Sanjana. "MEDICAL INSURANCE PREMIUM PREDICTION WITH MACHINE LEARNING." International Journal of Innovations in Engineering Research and Technology 11, no. 5 (May 18, 2024): 5–11. http://dx.doi.org/10.26662/ijiert.v11i5.pp5-11.

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A machine learning method for predicting health insurance rates is presented in this article. With healthcare expenditures becoming more complex, it is critical for insurance companies and policyholders to accurately estimate insurance prices. Utilizing a dataset that included medical history, demographic data, and other pertinent variables, a variety of machine learning techniques, such as ensemble methods and regression, were used to create prediction models. R-Squared and mean absolute error were two measures used to assess these models' performance. According to the developed models' results, insurance premiums can be predicted with accuracy, offering useful information for insurance counteragents. This approach has the potential to optimize pricing strategies, enhance risk assessment, and improve decision-making in the healthcare insurance sector. Machine Learning-Based Prediction of Medical Insurance Premiums Make predictions about health insurance companies based on personal traits. A dataset of policyholder attributes (such as age, gender, BMI, number of children, smoking behaviors, and geography) was gathered and preprocessed .Divide the data into sets for testing and training. Create and train a model for an artificial neural network with TensorFlow and Karas. R-squared metrics and mean R-squared error were used to assess the performance of the model. created a high R-Squared predictive model that was accurate. determined the main determinants of insurance rates. Machine learning has shown promise in estimating healthcare costs. This experiment demonstrates how well machine learning predicts medical insurance rates. Insurance companies may offer more individualized insurance plans, expedite the underwriting process, and help customers make well-informed decisions about their healthcare coverage by creating these predictive models. The created model can help policyholders make educated judgments and insurance companies establish proper prices. In the long run, our research helps the insurance industry enhance data-driven techniques, which benefits insurers as well as insured individuals in general.
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Ben Shoham, Ofir, and Nadav Rappoport. "CPLLM: Clinical prediction with large language models." PLOS Digital Health 3, no. 12 (December 6, 2024): e0000680. https://doi.org/10.1371/journal.pdig.0000680.

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We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts. For diagnostic predictions, we predicted whether patients would be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical medical records. We compared our results to various baselines, including Retain and Med-BERT, the latter of which is the current state-of-the-art model for disease prediction using temporal structured EHR data. In addition, we also evaluated CPLLM’s utility in predicting hospital readmission and compared our method’s performance with benchmark baselines. Our experiments ultimately revealed that our proposed method, CPLLM, surpasses all the tested models in terms of PR-AUC and ROC-AUC metrics, providing state-of-the-art performance as a tool for predicting disease diagnosis and patient hospital readmission without requiring pre-training on medical data. Such a method can be easily implemented and integrated into the clinical workflow to help care providers plan next steps for their patients.
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Takke, Kunal, Rameez Bhaijee, Avanish Singh, and Mr Abhay Patil. "Medical Disease Prediction using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 221–27. http://dx.doi.org/10.22214/ijraset.2022.42135.

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Abstract: There is a growing importance of healthcare and pandemic has proved that healthcare is an important aspect of an individual life. Most of the medical diagnoses require going to the doctor and fixing appointments for a consultation and sometimes to get accurate disease indications we have to wait for blood reports also we have to travel long distances to seek doctor consultation. When we are not feeling well the first thing we do is to check our temperature to get an estimate or baseline idea of our fever so we can consult our doctor if the temperature is high enough similarly a medical disease prediction application can be used to get a baseline idea of disease and can indicate us whether we should take immediate doctor consultation or not, or at least start some home-remedies for the same to find temporary relief. Combining machine learning with an application interface to interact with users provides opportunities for easy interaction with the users with the machine learning model to get more accurate predictions. Sometimes people feel reluctant to visit a hospital or consult a doctor for minor symptoms but there are cases where these minor symptoms may be indications of severe health problems hence medical disease prediction maybe useful to get a baseline prediction or estimation of disease in such cases. Keywords: machine Learning, prediction, medical diagnosis, healthcare
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R, Ashwini, S. M. Aiesha Afshin, Kavya V, and Prof Deepthi Raj. "Diabetes Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 544–49. http://dx.doi.org/10.22214/ijraset.2022.41143.

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Abstract: The concept of machine learning has quickly become very attractive to the healthcare industry. Predictions and analyzes made by the research community on medical data sets help with appropriate care and precautions in the prevention of disease. of machine learning, the types of algorithms that can help make decisions and predictions. We also discuss various applications of machine learning in the medical field, with a focus on diabetes prediction through machine learning. Diabetes is one of the most increasing diseases in the world and it requires continuous monitoring. To check this, we explore various machine learning algorithms which will help in early prediction of this disease. This work explains various aspects of machine learning, the types of algorithm which can help in decision making and prediction. The predictions and analysis made by the research community for medical dataset support the people by taking proper care and precautions by preventing diseases. Discuss various applications of machine learning in the field of medicine focusing on the prediction of diabetes through machine learning. Diabetes is one of the fastest-growing diseases in the world and requires constant monitoring. To verify this, we are exploring different machine learning algorithms that will help with this baseline prediction. Keywords: Decision Support Systems, Diabetes, Machine learning, Support vector Machine, Random Forest, K-Nearest Neighbor, Logistics Regression
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Reiz, Beáta, and Lehel Csató. "Bayesian Network Classifier for Medical Data Analysis." International Journal of Computers Communications & Control 4, no. 1 (March 1, 2009): 65. http://dx.doi.org/10.15837/ijccc.2009.1.2414.

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<p>Bayesian networks encode causal relations between variables using probability and graph theory. They can be used both for prediction of an outcome and interpretation of predictions based on the encoded causal relations. In this paper we analyse a tree-like Bayesian network learning algorithm optimised for classification of data and we give solutions to the interpretation and analysis of predictions. The classification of logical – i.e. binary – data arises specifically in the field of medical diagnosis, where we have to predict the survival chance based on different types of medical observations or we must select the most relevant cause corresponding again to a given patient record.<br />Surgery survival prediction was examined with the algorithm. Bypass surgery survival chance must be computed for a given patient, having a data-set of 66 medical examinations for 313 patients.</p>
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Dissertations / Theses on the topic "Medical Prediction"

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Shadabi, Fariba, and N/A. "Medical Outcome Prediction: A Hybrid Artificial Neural Networks Approach." University of Canberra. Information Sciences & Engineering, 2007. http://erl.canberra.edu.au./public/adt-AUC20070816.130444.

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This thesis advances the understanding of the application of artificial neural networks ensemble to clinical data by addressing the following fundamental question: What is the potentiality of an ensemble of neural networks models as a filter and classifier in a complex clinical situation? A novel neural networks ensemble classification model called Rules and Information Driven by Consistency in Artificial Neural Networks Ensemble (RIDCANNE) is developed for the purpose of prediction of medical outcomes or events, such as kidney transplants. The proposed classification model is based on combination of initial data preparations, preliminary classification by ensembles of Neural Networks, and generation of new training data based on criteria of highly accuracy and model agreement. Furthermore, it can also generate decision tree classification models to provide classification of data and the prediction results. The case studies described in this thesis are from a kidney transplant database and two well-known collections of benchmark data known as the Pima Indian Diabetes and Wisconsin Cancer datasets. An implication of this study is that further attention needs to be given to both data collection and preparation stages. This study revealed that even neural network ensemble models that are known for their strong generalization ability might not be able to provide a high level of accuracy for complex, noisy and incomplete clinical data. However, by using a selective subset of data points, it is possible to improve the overall accuracy. In summary, the research conducted for this thesis advances the current clinical data preparation and classification techniques in which the task is to extract patterns that contain higher information content from a sea of noisy and incomplete clinical data, and build accurate and transparent classifiers. The RIDC-ANNE approach improves an analyst�s ability to better understand the data. Furthermore, it shows great promise for use in clinical decision making systems. It can provide us with a valuable data mining tool with great research and commercial potential.
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Shadabi, Fariba. "Medical outcome prediction : a hybrid artificial neural networks approach /." Canberra, 2007. http://erl.canberra.edu.au/public/adt-AUC20070816.130444/index.html.

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Thesis (PhD) -- University of Canberra, 2007.
Thesis submitted in fulfilment of the requirements of the Degree of Doctor of Philosophy in Information Sciences and Engineering, University of Canberra, January 2007. Bibliography: leaves 110-127.
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Kyei-Blankson, Lydia S. "Predictive Validity, Differential Validity, and Differential Prediction of the Subtests of the Medical College Admission Test." Ohio University / OhioLINK, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1125524238.

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Sultan, Ahmad Hasane. "Prediction of medical technologists' scores on the MT (ASCP) certification examinations." Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-07282008-134142/.

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Meng, Mingyuan. "Deep Learning for Medical Image Registration and Radiomics-based Survival Prediction." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25391.

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With the importance of medical images for disease diagnosis and prognosis becoming widely recognized, medical image analysis has drawn much attention among researchers and clinicians. The goal of medical image analysis is to identify diagnostic and prognostic information from medical images and to establish diagnosis/prognosis models for assisting in clinical decision making and personalized treatments. Deep learning-based methods have achieved great success in computer vision research. This success is mainly attributed to its outstanding ability to learn high-level pattern representations from big data, and has motivated many investigators for applying deep learning-based algorithms in medical image analysis. The objectives of this thesis are to explore and develop deep learning methods for two medical image analysis tasks: medical image registration and radiomics. Firstly, we focused on medical image registration, a fundamental step of various medical image analysis tasks. We identified that a key challenge for accurate image registration is the variations in image appearance. Hence, we proposed an Appearance Adjustment Network (AAN) where we leverage anatomy edges, through an anatomy-constrained loss function, to generate an anatomy-preserving appearance transformation. We designed the AAN so that it can be readily embedded into a wide range of deep learning-based registration frameworks, to reduce the appearance differences between input image pairs and thereby improve registration accuracy. In this study, we experimented with Brain MRI data and observed improvements in registration accuracy. The results show that our AAN enhanced the baseline registration methods by roughly 2% in Dice score, while adding a fractional computational load. Secondly, we explored radiomics-based survival prediction of patients with advanced Nasopharyngeal Carcinoma (NPC). Radiomics refers to the extraction and analysis of high-dimensional quantitative features from non-invasive images. In this study, we incorporated deep learning into radiomics and developed an end-to-end multi-modality deep-learning model using pretreatment PET/CT images to predict 5-year progression-free survival. Furthermore, the deep-learning model was extensively compared with a large number of conventional radiomics methods for prognostic performance. The results show that the proposed deep-learning model outperformed conventional radiomics methods by roughly 5% in AUC. Our experimental results demonstrated that our models on two tasks outperform the existing methods, suggesting that deep learning is an effective tool for enhancing medical image analysis.
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Söderlund, Anne. "Physiotherapy Management, Coping and Outcome Prediction in Whiplash Associated Disorders (WAD)." Doctoral thesis, Uppsala University, Department of Public Health and Caring Sciences, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-601.

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The aims of the present thesis were to evaluate the management of acute WAD and to develop, describe and evaluate a cognitive behavioural approach for the physiotherapy management of long-term WAD as well as to study the predictors and mediating factors for long-term disability and pain after a whiplash injury.

Two approaches for acute and chronic WAD were evaluated in experimental studies. Fifty-nine patients with acute whiplash injury (study I) and 33 patients with chronic WAD (study V), were randomised into experimental and control groups. In addition, three chronic WAD patients participated in an experimental single case study (study IV). Home exercise programmes for patients with acute WAD were used in study I. In study IV a physiotherapy management with integrated components of cognitive-behavioural origin was tried for chronic WAD patients. In study V physiotherapy treatment in primary care units and a physiotherapy management with integrated components of cognitive-behavioural origin was tried for chronic WAD patients. Study I showed that a home exercise programme including training of neck and shoulder range of motion (ROM), relaxation and general advice, appears to be a sufficient treatment for most acute WAD patients. Further, the results of study IV and V suggest that cognitive behavioural components m be useful in physiotherapy treatment for patients with chronic WAD, but its contribution is not yet fully understood.

Study III showed that the significance of coping as an explanatory factor for disability increased during the one-year period after a whiplash injury. In study V it was concluded that self-efficacy is related to patients' use of different coping styles. A model to study coping as a mediator between self-efficacy and disability was therefore introduced. In a path-analytic framework, data from subjects in study I were re-analysed to illustrate a theoretical standpoint that emphasises the process of coping. With regard to disability, the proportion of explained variance increased from 39% at three weeks after the accident, to 79% at one-year follow-up. These results also show that coping has a crucial and mediating role between self-efficacy and disability. Positive long- term outcomes in WAD-patients would therefore be improved by, shortly after an accident, boosting self- efficacy and teaching patients to use active, adaptive coping strategies to manage their problems.

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Halvarsson, Klara. "Dieting and eating attitudes in girls : Development and prediction." Doctoral thesis, Uppsala University, Department of Public Health and Caring Sciences, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-538.

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The aims of the present thesis were to study: 1. reported eating attitudes, dieting behavior and body image over a 1-year period among preadolescent girls (age 7-8); 2. differences in eating attitudes and coping between groups of teenage girls differing in dieting frequency, and to assess changes with increasing age (age 13-17); and 3. to what extent eating attitudes, self-esteem and coping predict disturbed eating attitudes. A final aim was to explore differences in the reported wish to be thinner, dieting, and eating attitudes between two age-matched cohorts of girls in 1995 and 1999 (7-15 years).

The project is designed as a longitudinal prospective study, spanning seven years. 1300 girls in the ages (1995) 7, 9, 11, 13 and 15 years have been assessed annually for three consecutive years (1995-1997) (Main Cohort). An additional group matched for age with the original group was recruited in 1999 (Societal Cohort). The results suggest that dieting and the wish to be thinner starts as early as at 7 years of age, and that repeated dieting attempts correlate with disturbed eating attitudes. A marked increase of the wish to be thinner was evident in the 10- to 14-year age range, and significant increases in dieting attempts occurred mainly between ages 9 and 13. There were no differences between 1995 (Main Cohort) and 1999 (Societal Cohort) (except among 7 and 11-year-olds) with regard to dieting, the wish to be thinner and disturbed eating attitudes. Eating patterns and attitudes were shown to be the strongest predictors of disturbed eating attitudes three years later. Assessment of dieting, the wish to be thinner and eating attitudes is suggested BS a component in school health care.

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So, Hon-cheong, and 蘇漢昌. "Genetic architecture and risk prediction of complex diseases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B4452805X.

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Braithwaite, Emma Annette. "Neural networks for medical condition prediction : an investigation of neonatal respiratory disorder." Thesis, University of Edinburgh, 1998. http://hdl.handle.net/1842/12658.

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This thesis investigates how various signal processing techniques can be applied to diagnose problems in the medical domain. In particular it concentrates on breathing problems often experienced by premature babies who undergo artificial respiration. Medical Decision Support is an area of increasing research interest. The neonatal intensive care unit (NICU) is a prime example. This thesis describes the investigation of techniques to be used as the core of a decision support device in Edinburgh's NICU. At present physiological signals are taken from the patient and archived, little diagnostic use is made of these signals and no investigation has taken place into their diagnostic relevance. Within the scope of the work an investigation has taken place into the application area and some of its current problems have been identified. From these a physiological problem, respiratory disorder, was identified with characteristics which made it worthy of detailed study: it was extremely common, moreover expert knowledge and data about it already existed. With the current techniques the development of respiratory disorder is often missed or diagnosed too late. Signal processing techniques were evaluated with a view to applying them to predict the onset, or classify the development of, respiratory disorder, and a multi-layer perceptron network was chosen to perform as a classifier in the decision support tool. A number of tests were run which included an investigation of the efficiency of the chosen feature extraction techniques and the diagnostic relevance (with respect to the condition under investigation) of the signals being used to assist in diagnosis. Results show that at present the signals of greatest diagnostic relevance are not always used: a decision support device can be developed using a multi-layer perceptron classifier in combination with other signal processing techniques. The thesis also identifies other techniques where there is potential for improving the decision support tool's predictive and classification ability.
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Arens, Fanelo James. "The Altman corporation failure prediction model : applied among South African medical schemes." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/13084.

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Includes bibliographical references.
This study has a number of interrelated objectives that seek to understand and contextualize the Altman bankruptcy prediction model in the setting of the South African medical schemes over a ten year period (2002 to 2011). The main objective of this study is to validate the Altman Z₂ model amongst the medical schemes in South Africa; in terms of accurately classifying Z₂-scores of ≤ 1.23 and ≥ 2.9 into the a priori groups of failed and non-failed schemes. The average classification rates in the period 2002 to 2011 are as follows: 82% accuracy rate and 17.9% error rate. A linear trend line inserted in the graph shows the accuracy improving from 72% to 91% between the period 2003/2004 to 2011/2012. This outcome is consistent with the conclusion in previous studies (Aziz and Humayon, 2006: 27) that showed the accuracy rates in most failure prediction studies to be as follows: 84%, 88%, and 85% for statistical models, AEIS models and theoretical models respectively. Although this study validated the Altman model, further studies are required to test the rest of the study objectives under conditions where some of the assumptions are revised.
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Books on the topic "Medical Prediction"

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Geisser, Seymour. Diagnosis and Prediction. New York, NY: Springer New York, 1999.

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G, Taktak Azzam F., and Fischer Anthony C, eds. Outcome prediction in cancer. Amsterdam: Elsevier, 2007.

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M, Tanner J., ed. Assessment of skeletal maturity and prediction of adult height (TW3 method). 3rd ed. London: W.B. Saunders, 2001.

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Hein, Putter, ed. Dynamic prediction in clinical survival analysis. Boca Raton: CRC Press, 2012.

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B, Dressman J., and Lennernäs Hans, eds. Oral drug absorption: Prediction and assessment. New York: Marcel Dekker, 2000.

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Nicholson, Sean. Physician income prediction errors: Sources and implications for behavior. Cambridge, MA: National Bureau of Economic Research, 2002.

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D, Franklin Ronald, ed. Prediction in forensic and neuropsychology: Sound statistical practices. Mahwah, NJ: Lawrence Erlbaum Associates, 2003.

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Wiwanitkit, Viroj. Focus on climate change and health: Climate change and its causes, effects, and prediction. Hauppauge, NY: Nova Science Publishers, 2009.

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Woolley, Adam. A guide to practical toxicology: Evaluation, prediction, and risk. 2nd ed. New York: Informa Healthcare USA, 2008.

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Kil, David H. Pattern recognition and prediction with applications to signal characterization. Woodbury, N.Y: AIP Press, 1996.

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Book chapters on the topic "Medical Prediction"

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Schweikard, Achim, and Floris Ernst. "Motion Prediction." In Medical Robotics, 277–309. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22891-4_8.

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Conway, Deborah L. "Maternal Medical Conditions." In Stillbirth: Prediction, Prevention and Management, 117–31. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9781444398038.ch8.

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Dudley, Donald J. "Medical Management Including Delivery." In Stillbirth: Prediction, Prevention and Management, 229–41. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9781444398038.ch14.

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Nguyen, Dan. "Imaged-Based Dose Planning Prediction." In Medical Image Synthesis, 89–95. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-8.

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Marchevsky, David. "Correlation, Regression and Prediction." In Critical Appraisal of Medical Literature, 235–48. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4205-6_28.

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Bondio, Mariacarla Gadebusch, Francesco Spöring, and John-Stewart Gordon. "Introduction." In Medical Ethics, Prediction, and Prognosis, 1–7. 1 [edition]. | New York : Routledge, 2017. | Series: Routledge annals of bioethics ; 17: Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-1.

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Zhang, Xian-Ning, and Ji Zuo. "Genetic Disorders in Chinese Patients and Their Families." In Medical Ethics, Prediction, and Prognosis, 121–30. 1 [edition]. | New York : Routledge, 2017. | Series: Routledge annals of bioethics ; 17: Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-10.

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Spöring, Francesco. "Personalized Antidepressant Prescription." In Medical Ethics, Prediction, and Prognosis, 133–47. 1 [edition]. | New York : Routledge, 2017. | Series: Routledge annals of bioethics ; 17: Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-11.

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Lichtenthaler, Stefan F. "Predicting, Preventing, and Treating Alzheimer’s Disease." In Medical Ethics, Prediction, and Prognosis, 148–55. 1 [edition]. | New York : Routledge, 2017. | Series: Routledge annals of bioethics ; 17: Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-12.

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Lista, Simone, Francesco Garaci, Nicola Toschi, and Harald Hampel. "Early Detection, Prediction, and Prognosis of Alzheimer’s Disease." In Medical Ethics, Prediction, and Prognosis, 156–74. 1 [edition]. | New York : Routledge, 2017. | Series: Routledge annals of bioethics ; 17: Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-13.

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Conference papers on the topic "Medical Prediction"

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Lin, Sophia, Xinyu Dong, and Fusheng Wang. "FRISTS: High-Performing Interpretable Medical Prediction." In 2024 IEEE International Conference on Big Data (BigData), 7359–63. IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10826101.

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Yang, Wangying, Zitao Zheng, Shi Bo, Zhizhong Wu, Bo Zhang, and Yuanfang Yang. "Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), 798–802. IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10795963.

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Maeder, Anthony J. "Mammogram compression using adaptive prediction." In Medical Imaging 1995, edited by Yongmin Kim. SPIE, 1995. http://dx.doi.org/10.1117/12.207616.

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Koupaee, Mahnaz. "Mortality prediction using medical notes." In SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3297280.3297648.

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Mahfuri, Mahmoud, Taher M. Ghazal, Muhammad Mudassar, Shahan Yamin Siddiqui, Sajid Farooq, Nayab Kanwal, and Munir Ahmad. "Medical Diagnoses: Heart Disease Prediction." In 2023 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2023. http://dx.doi.org/10.1109/icbats57792.2023.10111497.

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Jirapatnakul, Artit C., Anthony P. Reeves, Tatiyana V. Apanasovich, Matthew D. Cham, David F. Yankelevitz, and Claudia I. Henschke. "Prediction of tumor volumes using an exponential model." In Medical Imaging, edited by Maryellen L. Giger and Nico Karssemeijer. SPIE, 2007. http://dx.doi.org/10.1117/12.710371.

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Ching, W., J. Robinson, and M. F. McEntee. "Comparing prediction models for radiographic exposures." In SPIE Medical Imaging, edited by Claudia R. Mello-Thoms and Matthew A. Kupinski. SPIE, 2015. http://dx.doi.org/10.1117/12.2081738.

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Guevara, Edgar, Francisco Javier González, Gerardo Herrera Corral, and Luis Manuel Montaño Zentina. "Prediction of Glucose Concentration by Impedance Phase Measurements." In MEDICAL PHYSICS: Tenth Mexican Symposium on Medical Physics. AIP, 2008. http://dx.doi.org/10.1063/1.2979285.

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Levitt, Tod S., Marcus W. Hedgcock, D. N. Vosky, and Vera M. Shadle. "Model-based prediction of phalanx radiograph boundaries." In Medical Imaging 1993, edited by Murray H. Loew. SPIE, 1993. http://dx.doi.org/10.1117/12.154555.

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Govinda K and Prasanna S. "Medical dialysis prediction using fuzzy rules." In 2015 International Conference on Soft-Computing and Networks Security (ICSNS). IEEE, 2015. http://dx.doi.org/10.1109/icsns.2015.7292418.

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Reports on the topic "Medical Prediction"

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Manski, Charles, John Mullahy, and Atheendar Venkataramani. Prediction with Differential Covariate Classification: Illustrated by Racial/Ethnic Classification in Medical Risk Assessment. Cambridge, MA: National Bureau of Economic Research, January 2025. https://doi.org/10.3386/w33350.

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Keshav, Dr Geetha, Dr Suwaibah Fatima Samer, Dr Salman Haroon, and Dr Mohammed Abrar Hassan. TO STUDY THE CORRELATION OF BMI WITH ABO BLOOD GROUP AND CARDIOVASCULAR RISK AMONG MEDICAL STUDENTS. World Wide Journals, February 2023. http://dx.doi.org/10.36106/ijar/2405523.

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Introduction: Advancements and increase in access to healthcare have increased the life expectancy in India from 32 years in 1947 to almost 70 years currently. Due to robust vaccination and basic health programs, most of the communicable diseases are kept under control. The disease burden is now skewed towards non-communicable diseases. It is an established fact that body mass index (BMI) is a reliable predictor of cardiovascular disease (CVD) later in life. Early prediction can decrease the disease load and enable early preventative measures. A more novel approach of connecting it with blood groups would yield profound results in predictability and subsequent management. This study was done to see correlation between BMI and known blood groups in order to predict the potential incidence of CVDs in medical students. Material and Method - A cross-sectional descriptive study was conducted in Bhaskar Medical College from September 2022 - November 2022. The sample population included 150- 1st year medical students chosen by Randomized sampling method. BMI was calculated based as weight in kilograms divided by the square of the height in meters (kg/m2). Discussion - Many studies conducted on the association of Blood groups with BMI yielded mixed and inconclusive results. On analysis of the data obtained from this study, O- positive blood group showed the highest inclination towards obesity i.e. 30 of the total participants. A-positive and B- positive blood groups were shown to have a lesser association with obesity i.e. 11 participants of the 150. These results were in accordance with a study done among female students by Shireen Javad et.al, nding blood group O to be the most prone to obesity.8 Incompatible to our results, a study conducted by Samuel Smith Isaac Okai et.al. found no signicant association between blood groups and BMI.10 Another study conducted by Christina Ravillo et.al. found that blood group O had the highest and blood group AB with lowest prevalence of obesity9. These ndings were similar to the results obtained in our study. To study the correlation of BMI with ABO blood group and Cardiovascula AIMS and OBJECTIVES Aim: - r risk among medical students. 1. Calculate and segregate the participants according to BM Objectives: - I using the standard formula provided by the WHO. 1. Determine Blood group using antisera 2. Evaluation of Lipid prole in obese individuals
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Bertrand, Andrew H. Understanding, Predicting, and Reducing Appointment No-Shows in a Military Medical Treatment Facility. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada422554.

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Bruno, Oscar P. Mathematical Prediction of the Physical Properties of Materials and Media. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada368323.

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Andrade, Jose E., and Ted Belytschko. Multi-Scale Prediction and Simulation of Localization Banding in Granular Media. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada563854.

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Smith, L. E., D. W. Brown, and R. E. Lowry. Prediction of the long term stability of polyester-based recording media. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.86-3474.

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Song, So Young, Erin Cho, Youn-Kyung Kim, and Theresa Hyunjin Kwon. Clothing Communication via Social Media: A Decision Tree Predictive Model. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-102.

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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Hughes, Patricia. The Asthma Management Program as a Predictor of Emergency Room Visits and Hospitalizations at David Grant USAF Medical Center. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada372311.

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Chen, Xiaole, Peng Wang, Yunquan Luo, Yi-Yu Lu, Wenjun Zhou, Mengdie Yang, Jian Chen, Zhi-Qiang Meng, and Shi-Bing Su. Therapeutic Efficacy Evaluation and Underlying Mechanisms Prediction of Jianpi Liqi Decoction for Hepatocellular Carcinoma. Science Repository, September 2021. http://dx.doi.org/10.31487/j.jso.2021.02.04.sup.

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Objective: The aim of this study was to assess the therapeutic effects of Jianpi Liqi decoction (JPLQD) in hepatocellular carcinoma (HCC) and explore its underlying mechanisms. Methods: The characteristics and outcomes of HCC patients with intermediate stage B who underwent sequential conventional transcatheter arterial chemoembolization (cTACE) and radiofrequency ablation (RFA) only or in conjunction with JPLQD were analysed retrospectively. The plasma proteins were screened using label-free quantitative proteomics analysis. The effective mechanisms of JPLQD were predicted through network pharmacology approach and partially verified by ELISA. Results: Clinical research demonstrated that the Karnofsky Performance Status (KPS), traditional Chinese medicine (TCM) syndrome scores, neutropenia and bilirubin, median progression-free survival (PFS), and median overall survival (OS) in HCC patients treated with JPLQD were superior to those in patients not treated with JPLQD (all P<0.05). The analysis of network pharmacology, combined with proteomics, suggested that 52 compounds targeted 80 potential targets, which were involved in the regulation of multiple signaling pathways, especially affecting the apoptosis-related pathways including TNF, p53, PI3K-AKT, and MAPK. Plasma IGFBP3 and CA2 were significantly up-regulated in HCC patients with sequential cTACE and RFA therapy treated with JPLQD than those in patients not treated with JPLQD (P<0.001). The AUC of the IGFBP3 and CA2 panel, estimated using ROC analysis for JPLQD efficacy evaluation, was 0.867. Conclusion: These data suggested that JPLQD improves the quality of life, prolongs the overall survival, protects liver function in HCC patients, and exhibits an anticancer activity against HCC. IGFBP3 and CA2 panels may be potential therapeutic targets and indicators in the efficacy evaluation for JPLQD treatment, and the effective mechanisms involved in the regulation of multiple signaling pathways, possibly affected the regulation of apoptosis.
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