Revolutionizing Patient Outcomes: The Role of Generative AI and Machine Learning in Predictive Analytics for Healthcare

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.

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Publication Details

Journal
Journal of Artificial Intelligence and Big Data Disciplines
Published
2024-12-11
DOI
https://doi.org/10.70179/js9jft76
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Revolutionizing Patient Outcomes: The Role of Generative AI and Machine Learning in Predictive Analytics for Healthcare

Valiki Dileep
Journal of Artificial Intelligence and Big Data Disciplines
Artificial Intelligence in Healthcare
article

Revolutionizing Patient Outcomes: The Role of Generative AI and Machine Learning in Predictive Analytics for Healthcare

Valiki Dileep
article en

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.

Journal of Artificial Intelligence and Big Data DisciplinesVol. 1(1)
International Institute of Information Technology, Hyderabad (IN)
Openalex Percentile: Top 59%
Artificial Intelligence in Healthcare
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