Relevant bibliographies by topics / Predictive Healthcare Modeling

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

Academic literature on the topic 'Predictive Healthcare Modeling'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Predictive Healthcare Modeling"

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Katreddy, Venkata Senareddy. "Predicting Risks in Healthcare Claims Using Advanced Data Processing and Machine Learning Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40802.

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Healthcare providers and insurers face significant challenges in managing claims, particularly in detecting fraudulent activities and predicting high-cost claims. This paper proposes a methodology for predicting risks in healthcare claims using data analysis and machine learning techniques. By processing large-scale claims data, analyzing patterns, and building predictive models, this approach aims to improve risk management, operational efficiency, and cost savings. Keywords: Healthcare Claims, Risk Prediction, Data Analysis, Predictive Modeling
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G. Ramachandra Rao and Mr.P.Venkata Siva, Dr K. R. R. Mohana Rao, Dr K. Kiran Kumar,. "Use of Predictive Modeling in Healthcare." International Journal for Modern Trends in Science and Technology 6, no. 8S (2020): 156–59. http://dx.doi.org/10.46501/ijmtstciet30.

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Chioma, Susan Nwaimo, Enoch Adegbola Ayodeji, and Daniel Adegbola Mayokun. "Transforming healthcare with data analytics: Predictive models for patient outcomes." GSC Biological and Pharmaceutical Sciences 27, no. 3 (2024): 025–35. https://doi.org/10.5281/zenodo.13383612.

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Healthcare organizations are increasingly leveraging data analytics to improve patient outcomes and enhance the efficiency of healthcare delivery. Predictive modeling, in particular, has emerged as a powerful tool for forecasting patient outcomes based on various data sources such as electronic health records, wearable devices, and genetic information. This paper provides an overview of the transformative role of data analytics in healthcare, with a specific focus on predictive models for patient outcomes. The introduction discusses the importance of data analytics in healthcare and outlines t
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Waheed, Shaikh Abdul, and P. Sheik Abdul Khader. "Healthcare Solutions for Children Who Stutter Through the Structural Equation Modeling and Predictive Modeling by Utilizing Historical Data of Stuttering." SAGE Open 11, no. 4 (2021): 215824402110581. http://dx.doi.org/10.1177/21582440211058195.

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Earlier studies established the role of demographic and temperamental features (DTFs) in the adaptation of childhood stuttering. However, these studies have been short on examining the latent interrelationships among DTFs and not utilizing them in predicting this disorder. This research article endeavors to examine latent interrelationships among DTFs in relation to childhood-stuttering. The purpose of the present is also to analyze whether DTFs can be utilized in predicting the likely risk of this speech disorder. Historical data on childhood stuttering was utilized for performing the invlove
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Chukwuka Emmanuel Eze, Geneva Tamunobarafiri Igwama, Ejike Innocent Nwankwo, and Ebube Victor Emeihe. "Predictive modeling for healthcare needs in the aging U.S. population: A conceptual exploration." Global Journal of Research in Science and Technology 2, no. 2 (2024): 094–102. http://dx.doi.org/10.58175/gjrst.2024.2.2.0074.

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The aging population in the United States poses significant challenges for healthcare systems, necessitating advanced strategies to anticipate and meet their healthcare needs. This review paper explores the potential of predictive modeling to address these challenges, offering a conceptual framework that integrates diverse data sources, including electronic health records (EHRs) and social determinants of health (SDOH). Key predictive modeling techniques, such as machine learning and statistical methods, are examined for their application in predicting patient outcomes, disease prevalence, and
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Chioma Susan Nwaimo, Ayodeji Enoch Adegbola, and Mayokun Daniel Adegbola. "Transforming healthcare with data analytics: Predictive models for patient outcomes." GSC Biological and Pharmaceutical Sciences 27, no. 3 (2024): 025–35. http://dx.doi.org/10.30574/gscbps.2024.27.3.0190.

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Healthcare organizations are increasingly leveraging data analytics to improve patient outcomes and enhance the efficiency of healthcare delivery. Predictive modeling, in particular, has emerged as a powerful tool for forecasting patient outcomes based on various data sources such as electronic health records, wearable devices, and genetic information. This paper provides an overview of the transformative role of data analytics in healthcare, with a specific focus on predictive models for patient outcomes. The introduction discusses the importance of data analytics in healthcare and outlines t
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Meek, Julie A. "10 Ways Predictive Modeling Is Changing Healthcare." CIN: Computers, Informatics, Nursing 27, no. 5 (2009): 334. http://dx.doi.org/10.1097/01.ncn.0000360475.69906.24.

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Ahmad, Ayas. "Predictive Analytics in Healthcare." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5624–26. http://dx.doi.org/10.22214/ijraset.2024.62897.

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Abstract: This paper delves into the core algorithms and techniques employed in healthcare predictive analytics, including machine learning, statistical modeling, and data mining. We explore the multifaceted applications of this technology, encompassing improved patient stratification for risk assessment, targeted interventions for disease prevention, and optimized resource allocation for healthcare systems. However, the implementation of predictive analytics necessitates careful consideration of ethical issues surrounding data privacy and potential biases within algorithms. Regulatory framewo
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Sharma, Vishal. "Integrating Machine Learning in Healthcare: Predictive Modeling for Mortality, Heart Failure, and Hospital Readmissions." South Asian Research Journal of Applied Medical Sciences 7, no. 01 (2025): 16–23. https://doi.org/10.36346/sarjams.2025.v07i01.003.

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Machine learning has emerged as a transformative tool in healthcare, enabling predictive analytics for disease progression, patient management, and clinical decision-making. This study integrates three critical areas: mortality trends in the USA, heart failure survival prediction using machine learning (ML) models, and hospital readmission forecasting with artificial intelligence (AI)-driven methodologies. Using datasets from national health statistics, clinical trial data, and electronic health records, this research applies Logistic Regression, Random Forest, Support Vector Machines (SVM), N
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Oh, Tae Ryom. "Integrating predictive modeling and causal inference for advancing medical science." Childhood Kidney Diseases 28, no. 3 (2024): 93–98. http://dx.doi.org/10.3339/ckd.24.018.

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Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they f
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Dissertations / Theses on the topic "Predictive Healthcare Modeling"

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Caglar, Toros. "A Queueing Theoretic Approach to Gridlock Prediction in Emergency Departments." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34556.

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When an emergency department (ED) decides that it is not going to be able to serve any more newly arriving patients, it declares "diversion". When an ED is on diversion, it suspends arrivals that can be controlled by forcing some or all of the incoming emergency medical system (EMS) transport units to search for alternate treatment facilities for their patients. This search causes both patients and EMS crew to loose valuable time. Contrary to the general belief that suggests diversions are not very common, the results of the American Hospital Association survey present an example where one
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Espinoza, Sofia Elizabeth. "Data mining methods applied to healthcare problems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44903.

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Growing adoption of health information technologies is allowing healthcare providers to capture and store enormous amounts of patient data. In order to effectively use this data to improve healthcare outcomes and processes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various data mining and statistical methods that could be applied to the type of datasets that are found in healthcare research. We discuss the process of identification of appropriate measures and statistical tools, the ana
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Corné, Josefine, and Amanda Ullvin. "Prediktiv analys i vården : Hur kan maskininlärningstekniker användas för att prognostisera vårdflöden?" Thesis, KTH, Skolan för teknik och hälsa (STH), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211286.

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Projektet genomfördes i samarbete med Siemens Healthineers i syfte att utreda möjligheter till att prognostisera vårdflöden. Det genom att undersöka hur big data tillsammans med maskininlärning kan utnyttjas för prediktiv analys. Projektet utgjordes av två fallstudier med mål att, baserat på data från tidigare MRT-undersökningar, förutspå undersökningstider för kommande undersökningar respektive identifiera patienter som riskerar att missa inbokad undersökning. Fallstudierna utfördes med hjälp av programmeringsspråket R och tre olika inbyggda funktioner för maskininlärning användes för att ta
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Daffue, Ruan Albert. "Applying patient-admission predictive algorithms in the South African healthcare system." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79897.

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Thesis (MScEng)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: Predictive analytics in healthcare has become one of the major focus areas in healthcare delivery worldwide. Due to the massive amount of healthcare data being captured, healthcare providers and health insurers are investing in predictive analytics and its enabling technologies to provide valuable insight into a large variety of healthcare outcomes. One of the latest developments in the field of healthcare predictive modelling (PM) was the launch of the Heritage Health Prize; a competition that challenges individuals from acr
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Ducey, Adam J. "Predicting Tablet Computer Use: An Extended Technology Acceptance Model." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4471.

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While information technology has rapidly changed work in the United States in the past 50 years, some businesses and industries have been slow to adopt new technologies. Healthcare is one industry that has lagged behind in information technology investment for a variety of reasons. Recent federal initiatives to encourage IT adoption in the healthcare industry provide an ideal context to study factors that influence technology acceptance. Data from 261 practicing pediatricians were collected to evaluate an extended Technology Acceptance Model. Results indicated that individual (i.e., perceived
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Faisal, Muhammad, Andy J. Scally, R. Howes, K. Beatson, D. Richardson, and Mohammed A. Mohammed. "A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation." 2018. http://hdl.handle.net/10454/16623.

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Yes<br>We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital (n=24696) and compared the performance of these models in data from another hospital (n=13477). We used two performance measures – the calibration slope and area under the curve (AUC). The logistic model performed reasonably
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Books on the topic "Predictive Healthcare Modeling"

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Roy, Sudipta, Lalit Mohan Goyal, and Mamta Mittal, eds. Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0538-3.

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Duncan, Ian. Healthcare Risk Adjustment & Predictive Modeling. ACTEX Learning, 2018.

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Healthcare risk adjustment and predictive modeling. ACTEX Publications, 2011.

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Miner, Gary D., Linda Miner, and Darrell L. Dean. HEALTHCARE's OUT SICK - PREDICTING a CURE - Solutions That WORK !!!!: Predictive Analytic Modeling, Decision Making, INNOVATIONS and Precision Medicine Necessary to Correct the Broken Healthcare Delivery System. Productivity Press, 2019.

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Miner, Gary D., Linda Miner, and Darrell L. Dean. HEALTHCARE's OUT SICK - PREDICTING a CURE - Solutions That WORK !!!!: Predictive Analytic Modeling, Decision Making, INNOVATIONS and Precision Medicine Necessary to Correct the Broken Healthcare Delivery System. Productivity Press, 2019.

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Miner, Gary D., Linda Miner, and Darrell L. Dean. HEALTHCARE's OUT SICK - PREDICTING a CURE - Solutions That WORK !!!!: Predictive Analytic Modeling, Decision Making, INNOVATIONS and Precision Medicine Necessary to Correct the Broken Healthcare Delivery System. Productivity Press, 2019.

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Miner, Gary D., Linda Miner, and Darrell L. Dean. HEALTHCARE's OUT SICK - PREDICTING a CURE - Solutions That WORK !!!!: Predictive Analytic Modeling, Decision Making, INNOVATIONS and Precision Medicine Necessary to Correct the Broken Healthcare Delivery System. Productivity Press, 2019.

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HEALTHCARE's OUT SICK - PREDICTING a CURE - Solutions That WORK !!!!: Predictive Analytic Modeling, Decision Making, INNOVATIONS and Precision Medicine Necessary to Correct the Broken Healthcare Delivery System. Taylor & Francis Group, 2019.

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Basu, Sanjay. Fundamentals. Edited by Sanjay Basu. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190667924.003.0001.

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In this chapter, the author defines and provides examples of several key terms used in public health and healthcare modeling research. The chapter begins by clarifying the differences between key terms used to describe rates of disease (incidence, prevalence, and mortality) as well as the performance characteristics of tests used to detect disease (sensitivity, specificity, positive predictive value, and negative predictive value), prevent or treat disease (odds ratios, relative risks), understand studies (case-control, cohort, and randomized controlled trials), and avoid common study problems
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Mittal, Mamta, Lalit Mohan Goyal, and Sudipta Roy. Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Springer, 2022.

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Book chapters on the topic "Predictive Healthcare Modeling"

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Zhang, Hao, Robert Meyer, Leyuan Shi, Wei Lu, and Warren D'Souza. "Predictive Modeling in Radiation Oncology." In Healthcare Analytics: From Data to Knowledge to Healthcare Improvement. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118919408.ch7.

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Chen, Yun, Fabio Leonelli, and Hui Yang. "Heterogeneous Sensing and Predictive Modeling of Postoperative Outcomes." In Healthcare Analytics: From Data to Knowledge to Healthcare Improvement. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118919408.ch17.

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Rana, Santu, Sunil Kumar Gupta, Dinh Phung, and Svetha Venkatesh. "Intervention-Driven Predictive Framework for Modeling Healthcare Data." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06608-0_41.

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Kaur, Simran, and Yasha Hasija. "Prognostic Modeling with the Internet of Healthcare Things Applications." In Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0538-3_7.

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Kavana Kumari, L., P. Suraksha, and M. Aishwarya. "Predictive modeling of autism spectrum disorder using machine learning algorithms." In Recent Trends in Healthcare Innovation. CRC Press, 2025. https://doi.org/10.1201/9781003501367-16.

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Sheikh, Md Mamun, Shahera Hossain, and Md Atiqur Rahman Ahad. "Predictive Modeling for Heatstroke Risk Forecasting Integrating Physiological Features Using Ensemble Classifier." In Activity, Behavior, and Healthcare Computing. CRC Press, 2025. https://doi.org/10.1201/9781032648422-17.

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Khurana, Pooja, Richa Gupta, Deepak Kumar, and Devendra Kumar. "Predictive Modeling of Interactions between Herbal and Conventional Medicines." In Handbook of Deep Learning Models for Healthcare Data Processing. CRC Press, 2025. https://doi.org/10.1201/9781003467281-6.

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Kummerfeld, Erich, Bryan Andrews, and Sisi Ma. "Foundations of Causal ML." In Health Informatics. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-39355-6_4.

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AbstractThe present chapter covers the important dimension of causality in ML both in terms of causal structure discovery and causal inference. The vast majority of biomedical ML focuses on predictive modeling and does not address causal methods, their requirements and properties. Yet these are essential for determining and assisting patient-level or healthcare-level interventions toward improving a set of outcomes of interest. Moreover causal ML techniques can be instrumental for health science discovery.
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Immonen, Mika, Heidi Huuskonen, Jouni Koivuniemi, and Jukka Hallikas. "Exploiting Machine Learning to Test Service Supply Scenarios: A Rescue Department Case." In Technology, Work and Globalization. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74779-3_13.

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Abstract This chapter assesses the use of machine learning (ML) to estimate service process performance and outlines questions concerning the modeling process. In particular, this study focuses on experimentation with service networks using trained artificial neural networks (ANNs). The theory is based on proactive supply chain risk management studies, the core of which is the utilization of big data, ML, and predictive analytics concerning supply networks. This article presents the case of a rescue service assessment in residential areas, where societal changes and healthcare reforms drive th
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Devi, M. Kiruthiga, T. Nalini, Kamalakannan Machap, and Rajan Kumar. "Computational Intelligence System for Healthcare." In Predictive Data Modelling for Biomedical Data and Imaging. River Publishers, 2024. http://dx.doi.org/10.1201/9781003516859-7.

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Conference papers on the topic "Predictive Healthcare Modeling"

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Verma, Somay, and Deepak Kumar. "Predictive Modeling For Diabetes Disease In Healthcare Systems." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739233.

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Merz, Collin, Angelo Galioto, and Md Liakat Ali. "Predictive Modeling for Patient Hospitalization: Enhancing Healthcare Outcomes." In 2024 IEEE URUCON. IEEE, 2024. https://doi.org/10.1109/urucon63440.2024.10850470.

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Wang, Xiaoyang, and Christopher C. Yang. "Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling." In 2025 IEEE 13th International Conference on Healthcare Informatics (ICHI). IEEE, 2025. https://doi.org/10.1109/ichi64645.2025.00015.

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Alqassem, Israa, Piyush Borole, Ammar Shaker, and Ajitha Rajan. "Tracing Pain: Predictive Modeling for Migraine and Headache Triggers." In 2025 IEEE 13th International Conference on Healthcare Informatics (ICHI). IEEE, 2025. https://doi.org/10.1109/ichi64645.2025.00017.

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Arya, Advika, Farwa Kazmi, and Sohail Zaidi. "Comparative Analysis of Machine Learning Techniques for Enhanced Predictive Modeling in Healthcare." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705168.

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Panda, Prasanta, Debaryaan Sahoo, and Debarjun Sahoo. "Advanced Predictive Modeling for Pneumonia Diagnosis to Revolutionize Healthcare with Transfer Learning." In 2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon). IEEE, 2024. https://doi.org/10.1109/nkcon62728.2024.10775139.

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Sharma, Deepak, Amit Singhal, Satvik Vats, and Bhavesh Kumar Sharma. "Advanced Clustering and Predictive Modeling Using AI/ML Algorithms in Smart Healthcare." In 2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET). IEEE, 2024. https://doi.org/10.1109/i3ceet61722.2024.10994147.

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Goyal, Shreya. "Age-Based Predictive Modeling of Emergency Department Visits Using Machine Learning." In 2024 IEEE 6th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE, 2024. https://doi.org/10.1109/ecbios61468.2024.10885430.

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Sharma, Harshita, and Ganesh Gopal Devarajan. "Predictive Modeling for Type-3 Diabetes: A Machine Learning Approach for Healthcare Management." In 2024 International Conference on Computing, Sciences and Communications (ICCSC). IEEE, 2024. https://doi.org/10.1109/iccsc62048.2024.10830451.

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Putalpattu, Muni Prasad, Kumbham Bhargavi, Megharani B. Mayani, Pilla Srinivas, Ayesha Siddiqa, and Mohan Kunkulagunta. "Advancing Predictive Modeling in Healthcare A Data Science Approach Utilizing AI-Driven Algorithms." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837024.

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