Zero Shot Health Trajectory Prediction Using Transformer

• URL: http://arxiv.org/abs/2407.21124v1
• Date: Tue, 30 Jul 2024 18:33:05 GMT
• Title: Zero Shot Health Trajectory Prediction Using Transformer
• Authors: Pawel Renc, Yugang Jia, Anthony E. Samir, Jaroslaw Was, Quanzheng Li, David W. Bates, Arkadiusz Sitek,
• Abstract summary: Enhanced Transformer for Health Outcome Simulation (ETHOS) is a novel application of the transformer deep-learning architecture for analyzing health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories.
• Score: 11.660997334071952
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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