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Zero shot health trajectory prediction using transformer.

Authors :
Renc, Pawel
Jia, Yugang
Samir, Anthony E.
Was, Jaroslaw
Li, Quanzheng
Bates, David W.
Sitek, Arkadiusz
Source :
NPJ Digital Medicine; 9/19/2024, Vol. 7 Issue 1, p1-10, 10p
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23986352
Volume :
7
Issue :
1
Database :
Complementary Index
Journal :
NPJ Digital Medicine
Publication Type :
Academic Journal
Accession number :
179739888
Full Text :
https://doi.org/10.1038/s41746-024-01235-0