Zero Shot Health Trajectory Prediction Using Transformer

TL;DR

This paper presents ETHOS, a transformer-based model that predicts future health trajectories from patient records without requiring labeled data, enabling personalized healthcare planning and decision-making.

Contribution

Introduction of ETHOS, a zero-shot transformer model for health outcome prediction that eliminates the need for labeled data and fine-tuning in healthcare analytics.

Findings

ETHOS accurately predicts health trajectories from patient timelines.

ETHOS can simulate treatment pathways considering patient-specific factors.

The model advances foundation models in healthcare without labeled data.

Abstract

Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories, leveraging a zero-shot learning approach. ETHOS represents a significant advancement in foundation model development for healthcare analytics, eliminating the need for labeled data and model fine-tuning. Its ability to simulate various treatment pathways and consider patient-specific factors positions ETHOS as a tool for care optimization and addressing biases in healthcare delivery. Future developments will expand ETHOS' capabilities to incorporate a wider range of data types and data sources. Our work demonstrates a pathway toward accelerated AI development and deployment in healthcare.