Bibliografías temáticas / Medical Prediction

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Literatura académica sobre el tema "Medical Prediction"

Autor: Grafiati
Publicado: 29 de marzo de 2025
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "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 (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 P
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Sabarinath, U. S. "Medical Insurance Cost Prediction." Indian Journal of Data Communication and Networking (IJDCN) 4, no. 4 (2024): 1–4. https://doi.org/10.54105/ijdcn.D5037.04040624.

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<strong>Abstract:</strong> 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 severa
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K. Tanusha, M. Varsha, E. Naveen Kumar, K. Pavan Kalayan, and P.M. Suresh. "Medical Insurance Price Prediction Using ML." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 2111–16. https://doi.org/10.47392/irjaem.2025.0333.

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The rising significance of health insurance in the aftermath of the COVID-19 pandemic has spurred numerous initiatives aimed at better understanding and managing medical insurance costs. This study presents a machine learning-based approach for predicting health insurance expenses using a dataset sourced from Kaggle. The primary objective is to develop a predictive system that assists individuals in making cost-effective insurance decisions and supports policymakers in identifying and regulating high-cost providers. Various regression algorithms were explored to capture the complex relationshi
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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 (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
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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
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Kannan, S., G. Premalatha, M. Jamuna Rani, et al. "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 present
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P., Renukadevi. "Lossless Medical Image Compression by Multi Oriented Prediction Technique." International Journal of Psychosocial Rehabilitation 24, no. 5 (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 (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' resul
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Ben Shoham, Ofir, and Nadav Rappoport. "CPLLM: Clinical prediction with large language models." PLOS Digital Health 3, no. 12 (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 diseas
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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 (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 medic
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Tesis sobre el tema "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 combina
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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.<br>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 fro
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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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<p>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. </p><p>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 p
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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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<p>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 (7-1
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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 u
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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.<br>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 Z&#8322; model amongst the medical schemes in South Africa; in terms of accurately classifying Z&#8322;-scores of &#8804; 1.23 and &#8805; 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:
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Libros sobre el tema "Medical Prediction"

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

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

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

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

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

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

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

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

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

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

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Capítulos de libros sobre el tema "Medical Prediction"

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Schweikard, Achim, and Floris Ernst. "Motion Prediction." In Medical Robotics. 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. 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. 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. 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. 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. 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. 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. 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. 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. Routledge, 2017. http://dx.doi.org/10.4324/9781315208084-13.

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Actas de conferencias sobre el tema "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). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10826101.

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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. ACM, 2019. http://dx.doi.org/10.1145/3297280.3297648.

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Mahfuri, Mahmoud, Taher M. Ghazal, Muhammad Mudassar, et al. "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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Tashakkori, Rahman, John M. Tyler, and Oleg S. Pianykh. "Prediction of medical images using wavelets." In AeroSense 2000, edited by Harold H. Szu, Martin Vetterli, William J. Campbell, and James R. Buss. SPIE, 2000. http://dx.doi.org/10.1117/12.381694.

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Informes sobre el tema "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. National Bureau of Economic Research, 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, 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
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Abdelhay, Osama, Adam Shatnawi, Hasan Najadat, and Taghreed Altamimi. Protocol for a Scoped Systematic Review and Meta-Regression of Resampling Methods in Imbalanced Medical Datasets: Data-Level and Algorithm-Level Strategies in Clinical Prediction Models. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.5.0026.

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Roldán-Ferrín, Felipe, and Julián A. Parra-Polania. ENHANCING INFLATION NOWCASTING WITH ONLINE SEARCH DATA: A RANDOM FOREST APPLICATION FOR COLOMBIA. Banco de la República, 2025. https://doi.org/10.32468/be.1318.

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This paper evaluates the predictive capacity of a machine learning model based on Random Forests (RF), combined with Google Trends (GT) data, for nowcasting monthly inflation in Colombia. The proposed RF-GT model is trained using historical inflation data, macroeconomic indicators, and internet search activity. After optimizing the model’s hyperparameters through time series cross-validation, we assess its out-of-sample performance over the period 2023–2024. The results are benchmarked against traditional approaches, including SARIMA, Ridge, and Lasso regressions, as well as professional forec
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Bertrand, Andrew H. Understanding, Predicting, and Reducing Appointment No-Shows in a Military Medical Treatment Facility. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. 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. Iowa State University, Digital Repository, 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, 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 sam
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