  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.
