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Revolutionizing Patient Outcomes: The Role of Generative AI and Machine Learning in Predictive Analytics for Healthcare
Published in October-December 2024 (Vol. 1, Issue 1, 2024)

Keywords
predictive analyticshealthcare analyticspatient outcomeshospital readmissionstreatment cost reductionelectronic health recordsgenerative modelspredictive healthcare modelinglaboratory data analysismedication data analysissocio-economic datamental health analyticscontextual data relationshipsmodel interpretabilityhealthcare data modelingbenchmark datacase studiesdata-driven healthcareAI in healthcarehealthcare decision supportpredictive modeling framework
Abstract
For the healthcare industry, predictive analytics offer revolutionary benefits for improving patient outcomes, reducing hospital readmissions, and lowering treatment costs. The increasing adoption of electronic health records allows the modeling of laboratory results, medications, and socio-economic data, as well as mental health, among others. We emphasize the opportunities that generative models offer for predictive healthcare analytics and the necessity for healthcare analytics to contextualize data relationships. We analyze predictive models, understand our contextual data relationships, interpret our results, expose them, and understand why models are learning certain relationships. We make use of benchmark data and case studies to illustrate our points. Our discussion concludes by offering a framework and a departure point for future related research.
Authors (1)
Valiki Dileep
Software Architect Capadobe La...Software Architect Capadobe Labs, Hyderabad, IndiaSoftware Architect Capadobe Labs, Hyderabad, IndiaSoftware Architect Capadobe Labs, Hyderabad, India
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Article Information
Published in:
October-December 2024 (Vol. 1, Issue 1, 2024)- Article ID:
- jaibdd110002
- Paper ID:
- JAIBDD-01-000002
- Published Date:
- 2026-02-10
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How to Cite
, V. (2026). Revolutionizing Patient Outcomes: The Role of Generative AI and Machine Learning in Predictive Analytics for Healthcare. Journal of Artificial Intelligence and Big Data Disciplines (JAIBDD), 1(1), xx-xx. DOI:https://doi.org/10.70179/js9jft76
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