Renc Pawel, Jia Yugang, Samir Anthony E, Was Jaroslaw, Li Quanzheng, Bates David W, Sitek Arkadiusz
Massachusetts General Hospital, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.
NPJ Digit Med. 2024 Sep 19;7(1):256. doi: 10.1038/s41746-024-01235-0.
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.
将现代机器学习与临床决策相结合，对于缓解医疗保健领域不断增加的成本和复杂性具有巨大潜力。我们引入了用于健康结果模拟的增强型变压器（ETHOS），这是变压器深度学习架构在分析高维、异构和间歇性健康数据方面的一种新颖应用。ETHOS使用患者健康时间线（PHTs）进行训练，PHTs是健康事件的详细、标记化记录，通过零样本学习方法来预测未来的健康轨迹。ETHOS代表了医疗保健分析基础模型开发的重大进展，无需标记数据和模型微调。它能够模拟各种治疗途径并考虑患者特定因素，使ETHOS成为优化护理和解决医疗服务中偏差的工具。未来的发展将扩展ETHOS的能力，以纳入更广泛的数据类型和数据源。我们的工作展示了一条在医疗保健领域加速人工智能开发和部署的途径。
