Language
English
Publication Date
9-19-2024
Journal
npj Digital Medicine
DOI
10.1038/s41746-024-01235-0
PMID
39300208
PMCID
PMC11412988
PubMedCentral® Posted Date
9-19-2024
PubMedCentral® Full Text Version
Post-print
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.
Keywords
Health care, Diseases
Published Open-Access
yes
Recommended Citation
Was, Jaroslaw; Li, Quanzheng; Bates, David W; et al., "Zero Shot Health Trajectory Prediction Using Transformer" (2024). Faculty, Staff and Students Publications. 8468.
https://digitalcommons.library.tmc.edu/baylor_docs/8468