Zero Shot Health Trajectory Prediction Using Transformer
<|reference_start|>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 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.<|reference_end|>
@article{renc2024zero,
    title={Zero Shot Health Trajectory Prediction Using Transformer},
    author={Pawel Renc, Yugang Jia, Anthony E. Samir, Jaroslaw Was, Quanzheng Li,
  David W. Bates, Arkadiusz Sitek},
    journal={arXiv preprint arXiv:2407.21124},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21124},
    primaryClass={cs.LG cs.AI cs.CY}
}