Relevant bibliographies by topics / Predictive healthcare

Contents

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

Academic literature on the topic 'Predictive healthcare'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Predictive healthcare"

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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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M, Mrs Adithi, ,. Pavan Kumar R, Priya Y. S, Sneha B. S, and Vaishnavi O. "Smart Healthcare Prediction Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39440.

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In this paper, the utilization of machine learning techniques in the healthcare system is introduced. As the healthcare industry generates increasingly vast amounts of data daily, manual processing by humans becomes impractical for prompt disease diagnosis and treatment decisions. To address this challenge, data management techniques and machine learning algorithms are explored in healthcare applications to facilitate more accurate decision-making processes. Detailed descriptions of medical data are provided, enhancing various facets of healthcare applications through the adoption of this cutt
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Hariom, Rajput* Sanoop Kumar Tiwari. "Detection Of Diseases And Predictive Analytic." International Journal in Pharmaceutical Sciences 1, no. 11 (2023): 180–91. https://doi.org/10.5281/zenodo.10084620.

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Predictive analytics plays a vital role in transforming healthcare by improving patient care, reducing costs, and optimizing resource allocation. As technology continues to advance and healthcare systems become more data- driven, the benefits of predictive analytics are likely to expand, contributing to better healthcare outcomes for individuals and populations alike. Predictive analytics is transforming the healthcare landscape by enhancing early disease detection and prevention. By harnessing the power of data and artificial intelligence, healthcare providers can offer more personalized, eff
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Boonprasope, Anuwat, and Korrakot Yaibuathet Tippayawong. "Predicting Healthcare Mutual Fund Performance Using Deep Learning and Linear Regression." International Journal of Financial Studies 12, no. 1 (2024): 23. http://dx.doi.org/10.3390/ijfs12010023.

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Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing research on predicting future prices of healthcare securities has been predominantly reliant on historical trading data, limiting predictive accuracy and scope. This study aims to overcome these constraints by integrating a diverse set of twelve external factors spanning economic, industrial, and company-specific domains to enhance predictive models.
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Kailash, Alle. "AI in Healthcare: Predictive Analytics and Diagnostics." Journal of Scientific and Engineering Research 7, no. 9 (2020): 233–37. https://doi.org/10.5281/zenodo.13347491.

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Predictive analytics and decision support systems are changing patient care in artificial intelligence (AI) in healthcare. Through the identification of trends and risk variables, predictive analytics ease early illness prevention and diagnosis, improving patient outcomes and enabling cost-effective healthcare. By using unique patient data to create customized therapies that maximize benefits and reduce side effects, machine learning enables individualized treatment strategies. AI-driven algorithms improve diagnostic precision in medical imaging by delivering quick and correct evaluations. Hea
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Cypher, Rebecca L. "Predictive Analysis in Healthcare." Journal of Perinatal & Neonatal Nursing 35, no. 4 (2021): 298–301. http://dx.doi.org/10.1097/jpn.0000000000000605.

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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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Sugarwar, Kalyani S., and Santanu Sikdar. "Artificial Intelligence Applications in Predictive Healthcare Systems." Journal of Advances and Scholarly Researches in Allied Education 22, no. 01 (2025): 322–33. https://doi.org/10.29070/s2zg4656.

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Predictive systems made possible by artificial intelligence (AI) are revolutionising healthcare by allowing for more precise, rapid, and individualised medical procedures. Using data analytics, NLP, and machine learning algorithms, this article delves into the ways AI is being applied to predictive healthcare, specifically in the areas of illness risk prediction, treatment plan optimisation, and patient outcome improvement. Using massive datasets derived from genetic information, electronic health records, and real-time monitoring equipment, predictive algorithms seek out trends and outliers t
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de Korte, Maud H., Gertjan S. Verhoeven, Arianne M. J. Elissen, Silke F. Metzelthin, Dirk Ruwaard, and Misja C. Mikkers. "Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification." European Journal of Health Economics 21, no. 8 (2020): 1121–29. http://dx.doi.org/10.1007/s10198-020-01213-9.

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Abstract Background The Netherlands is currently investigating the feasibility of moving from fee-for-service to prospective payments for home healthcare, which would require a suitable case-mix system. In 2017, health insurers mandated a preliminary case-mix system as a first step towards generating information on client differences in relation to care use. Home healthcare providers have also increasingly adopted standardized nursing terminology (SNT) as part of their electronic health records (EHRs), providing novel data for predictive modelling. Objective To explore the predictive potential
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Gupta, Saurabh. "The Role of AI in Predictive Healthcare Analytics." International Journal of Science and Research (IJSR) 13, no. 11 (2024): 1760–64. https://doi.org/10.21275/sr241130150603.

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Dissertations / Theses on the topic "Predictive healthcare"

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Wickramasuriya, Dilranjan S. "Predictive Analytics in Cardiac Healthcare and 5G Cellular Networks." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6980.

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This thesis proposes the use of Machine Learning (ML) to two very distinct, yet compelling, applications – predicting cardiac arrhythmia episodes and predicting base station association in 5G networks comprising of virtual cells. In the first scenario, Support Vector Machines (SVMs) are used to classify features extracted from electrocardiogram (EKG) signals. The second problem requires a different formulation departing from traditional ML classification where the objective is to partition feature space into constituent class regions. Instead, the intention here is to identify temporal pattern
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Victors, Mason Lemoyne. "A Classification Tool for Predictive Data Analysis in Healthcare." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5639.

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Hidden Markov Models (HMMs) have seen widespread use in a variety of applications ranging from speech recognition to gene prediction. While developed over forty years ago, they remain a standard tool for sequential data analysis. More recently, Latent Dirichlet Allocation (LDA) was developed and soon gained widespread popularity as a powerful topic analysis tool for text corpora. We thoroughly develop LDA and a generalization of HMMs and demonstrate the conjunctive use of both methods in predictive data analysis for health care problems. While these two tools (LDA and HMM) have been used in co
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Gligorijevic, Djordje. "Predictive Uncertainty Quantification and Explainable Machine Learning in Healthcare." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/520057.

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Computer and Information Science<br>Ph.D.<br>Predictive modeling is an ever-increasingly important part of decision making. The advances in Machine Learning predictive modeling have spread across many domains bringing significant improvements in performance and providing unique opportunities for novel discoveries. A notably important domains of the human world are medical and healthcare domains, which take care of peoples' wellbeing. And while being one of the most developed areas of science with active research, there are many ways they can be improved. In particular, novel tools developed ba
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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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Tekieh, Mohammad Hossein. "Analysis of Healthcare Coverage Using Data Mining Techniques." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/20547.

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This study explores healthcare coverage disparity using a quantitative analysis on a large dataset from the United States. One of the objectives is to build supervised models including decision tree and neural network to study the efficient factors in healthcare coverage. We also discover groups of people with health coverage problems and inconsistencies by employing unsupervised modeling including K-Means clustering algorithm. Our modeling is based on the dataset retrieved from Medical Expenditure Panel Survey with 98,175 records in the original dataset. After pre-processing the data, includi
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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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Lin, Yu-Kai, Hsinchun Chen, Randall A. Brown, Shu-Hsing Li, and Hung-Jen Yang. "HEALTHCARE PREDICTIVE ANALYTICS FOR RISK PROFILING IN CHRONIC CARE: A BAYESIAN MULTITASK LEARNING APPROACH." SOC INFORM MANAGE-MIS RES CENT, 2017. http://hdl.handle.net/10150/625248.

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Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principl
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Jiao, Weiwei. "Predictive Analysis for Trauma Patient Readmission Database." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492718909631318.

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Cheng, Chih-Wen. "Development of integrated informatics analytics for improved evidence-based, personalized, and predictive health." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54872.

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Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data
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Books on the topic "Predictive healthcare"

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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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Singh Bhati, Bhoopesh, Dimple Tiwari, and Nitesh Singh Bhati. IoT and AI-Enabled Healthcare Solutions and Intelligent Disease Prediction. CRC Press, 2025. https://doi.org/10.1201/9781003509608.

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Gary, Miner, Miner Linda, and Dean Darrell. Healthcare’s Out Sick – Predicting A Cure – Solutions That Work !!!! Productivity Press, 2019. http://dx.doi.org/10.4324/9780429506932.

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SUBBHURAAM. Predictive Analytics Healthcare Chroni. Institute of Physics Publishing, 2021.

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SUBBHURAAM. Predictive Analytics Healthcare Chroni. Institute of Physics Publishing, 2021.

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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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Banerjee, Sourav, Chinmay Chakraborty, and Kousik Dasgupta. Green Computing and Predictive Analytics for Healthcare. Taylor & Francis Group, 2020.

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Nelson, John W., Jayne Felgen, and Mary Ann Hozak, eds. Using Predictive Analytics to Improve Healthcare Outcomes. Wiley, 2021. http://dx.doi.org/10.1002/9781119747826.

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Banerjee, Sourav, Chinmay Chakraborty, and Kousik Dasgupta. Green Computing and Predictive Analytics for Healthcare. Taylor & Francis Group, 2020.

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Book chapters on the topic "Predictive healthcare"

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El Morr, Christo, and Hossam Ali-Hassan. "Descriptive, Predictive, and Prescriptive Analytics." In Analytics in Healthcare. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04506-7_3.

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Chan, Willie Sai Ho. "Taiwan’s Healthcare Report." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_11.

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Chanchaichujit, Janya, Albert Tan, Fanwen Meng, and Sarayoot Eaimkhong. "Optimization, Simulation and Predictive Analytics in Healthcare." In Healthcare 4.0. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8114-0_5.

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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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Mohapatra, Subasish, Subhadarshini Mohanty, Jyoti Ranjan Nayak, and Sanjeeb Kumar Nayak. "Predictive analytics in healthcare systems." In Intelligent Computing Techniques and Applications. CRC Press, 2025. https://doi.org/10.1201/9781003658221-64.

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Döring, Andrea, and Friedemann Paul. "The German Healthcare System." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_4.

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Gonçalves, Filipe, Ruben Pereira, João Ferreira, José Braga Vasconcelos, Fernando Melo, and Iria Velez. "Predictive Analysis in Healthcare: Emergency Wait Time Prediction." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01746-0_16.

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Spiru, Luiza, Răzvan Ioan Traşcu, Ileana Turcu, and Mircea Mărzan. "Perpetual Transitions in Romanian Healthcare." In Advances in Predictive, Preventive and Personalised Medicine. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4602-2_7.

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Lee, Eva K. "Predictive Analytics: Classification in Medicine and Biology." In Healthcare Analytics: From Data to Knowledge to Healthcare Improvement. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118919408.ch6.

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Anand, Gurjapna, and Priyanka Vashisht. "Predictive and Descriptive Analytics in Healthcare." In Data-Driven Analytics for Healthcare. Apple Academic Press, 2024. http://dx.doi.org/10.1201/9781003558743-1.

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Conference papers on the topic "Predictive healthcare"

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Kaushik, Priyanka, Manish Kumar Goyal, Sanskriti Mehta, Sharmistha Roy, Abdul Wadood Siddiqui, and Abhishek Yadav. "Predictive Analytics in Healthcare: Improving Patient Outcomes." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10894852.

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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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P, Kumar, Vishva A, and Vinoth Raj G. "Predictive Analytics for Healthcare using Statistical Analysis Technique." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10895288.

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Indrakumari, R., Vishal Pandey, Ashish Kumar Sinha, and Ankesh Kumar. "Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895940.

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Bogiri, Nagaraju, Vikas Maral, Sai Pagar, Anirudh Mane, Om Babde, and Vaibhav Pandey. "Predictive Analytics in Virtual Healthcare using Decision-Tree." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011582.

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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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Naik, Shibaa, Pushpak Kumar, Sneha Saha, Samriddha Das Bairagya, Darshan Rawat, and Santosh Kumar Baliarsingh. "Predictive Healthcare Analytics: A Multidisease Approach using Logistic Regression." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725194.

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Jagtap, Krutika, Mrunal Badshah, Snehal Pradhan, Rasika Kulkarni, and Smita Bhanap. "Pregnancy mode Detection Predictive Model for Enhanced Maternal Healthcare." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828784.

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Shetty, Shamanth R., and Praveena Kumari M K. "Personalized Predictive Healthcare Using Machine Learning and Generative AI." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987453.

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Reports on the topic "Predictive healthcare"

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Latorre, Lucía, Eduardo Rego, Lorenzo De Leo, and Mariana Gutierrez. Tech Report: Digital Twins. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013166.

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Digital twins are finding innovative applications in a wide range of industries. In manufacturing, they enable product design optimization, predictive machinery maintenance, and customized production. Healthcare will benefit from precise diagnostics, personalized treatments, and advanced surgical planning. In city planning, they support efficient urban design and complex situation management. Regarding energy, they promote the efficiency and sustainability of systems and infrastructure. Finally, in the agricultural sector, they improve crop management, resource use and animal welfare.
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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Ross, Grant, Martin Skelly, Chris Lim, Andrew Cook, Amy Rogers, and Suzanne Zaremba. A workshop about health futures by Futures Collective Dundee. University of Dundee, 2025. https://doi.org/10.20933/100001410.

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This publication documents Health Futures, a speculative co-design project led by Futures Collective Dundee in collaboration with young people across the city. Through a series of creative workshops, participants aged between early teens and young adulthood were invited to imagine, design, and prototype health innovations for the year 2049. The project employed speculative design methods—including futurecasting, artefact creation, and scenario-based thinking—to support meaningful youth engagement in health technology research. Outputs included a range of conceptual artefacts, such as robotic c
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Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317444.

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The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow a
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Cherian, Jerald, Jodi Segal, Ritu Sharma, Allen Zhang, Eric Bass, and Michael Rosen. Patient Safety Practices Focused on Sepsis Prediction and Recognition. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepc_mhs4sepsis.

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Objectives. Patient safety practices (PSPs) focused on sepsis prediction and recognition, encompass interventions designed to identify patients with sepsis early and improve timely adherence to guidelines. Our objectives were to review the evidence published after the previous Making Healthcare Safer (MHS) report to determine the effectiveness of sepsis prediction and recognition PSPs on patient safety related outcomes. Methods. We searched PubMed and the Cochrane library for systematic reviews and primary studies published from January 2018 through August 2023, supplemented by gray literature
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Gupta, Shikhar, Mehtab Ahmed, Sayema ., Azam Haseen, and Saif Quaiser. Relevance of Preoperative Vessel Mapping and Early Postoperative Ultrasonography in Predicting AV Fistula Failure in Chronic Kidney Disease Patients. Science Repository, 2024. http://dx.doi.org/10.31487/j.rdi.2023.02.02.

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Introduction: The increasing prevalence of chronic kidney disease (CKD), coupled with advancements in the diagnosis and treatment of renal diseases and improvements in life expectancy, has led to a greater number of patients requiring hemodialysis. The preferred method of vascular access for hemodialysis is AV fistula formation; however, it is associated with a high rate of failure. In our prospective study, we focused on 40 CKD patients planned for initiation of maintenance hemodialysis. Methods: We employed preoperative ultrasound mapping to assess cephalic vein diameter, compressibility, an
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Froemke, Cecily. Enhancing Value-Based Healthcare with Reconstructability Analysis: Predicting Risk for Hip and Knee Replacements. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.5656.

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MR MSK Cartilage for Joint Disease, Consensus Profile. Chair Thomas Link and Xiaojuan Li. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2021. http://dx.doi.org/10.1148/qiba/20210925.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed. This QIBA Profile (MR-based cartilage compositional biomarkers (T1ρ, T2) ) addresses the application of T1ρ and T2 for the quantification of cartilage composition, which c
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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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