Zero Shot Health Trajectory Prediction Using Transformer

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 analy...

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Bibliographic Details
Published in:arXiv.org 2024-07
Main Authors: Renc, Pawel, Jia, Yugang, Samir, Anthony E, Was, Jaroslaw, Li, Quanzheng, Bates, David W, Sitek, Arkadiusz
Format: Article
Language:English
Subjects:
Cost analysis
Data analysis
Deep learning
Dimensional analysis
Health care
Machine learning
Trajectory analysis
Transformers
Zero-shot learning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422