Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Area of Science:

• Medicine
• Computer Science
• Data Science

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly utilized in healthcare, with over 100,000 PubMed articles published between 2018-2022.
• Existing reviews often focus on specific subfields, leaving a gap in comprehensive analysis of AI/ML for longitudinal patient health prediction.

Purpose of the Study:

• To provide a comprehensive review of AI and ML methods applied to longitudinal analysis and prediction of individual patient health status.
• To identify research gaps and future directions in personalized disease pathway modeling.

Main Methods:

• Overview of commonly employed AI and ML methodologies in medical research.
• Analysis of specific medical applications and case studies utilizing these AI/ML models.
• Discussion of the strengths and limitations of current research in the field.

Main Results:

• AI and ML offer significant potential for predicting patient health trajectories and enabling personalized medicine.
• Current studies show promise but also highlight limitations in methodology and application scope.
• The review synthesizes existing knowledge to inform future research endeavors.

Conclusions:

• A comprehensive understanding of AI/ML in longitudinal health analysis is crucial for advancing personalized disease management.
• Future research should focus on refining predictive models for health status deterioration.
• This review serves as a guide for researchers planning future work in predictive health analytics.