Probabilistic Machine Learning for Healthcare

TL;DR

This paper reviews how probabilistic machine learning models can enhance healthcare by improving data interpretation, calibration, handling missing data, and supporting phenotyping, generative modeling, and reinforcement learning applications.

Contribution

It provides a comprehensive overview of the applications and benefits of probabilistic machine learning in healthcare, highlighting recent advances and challenges.

Findings

Probabilistic models improve calibration and data completeness.

They enable better phenotyping and generative modeling in clinical settings.

Reinforcement learning benefits from probabilistic approaches in healthcare.

Abstract

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.