Clinical foundation model for structured clinical data

结构化临床数据的临床基础模型

基本信息

批准号：
10639397
负责人：
Laila Rasmy Gindy Bekhet
金额：
$ 35.1万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10639397
关键词：
Architecture Attention COVID-19 patient Chemicals Clinical Clinical Data Code Data Data Element Data Protection Data Sources Development Diagnosis Electronic Health Record Engineering Evaluation Event Fostering Foundations Goals Health Health Care Costs Healthcare Systems Heart failure Intake Journals Knowledge Learning Long COVID Malignant neoplasm of pancreas Methodology Methods Modeling Molecular Natural Language Processing Patient risk Patient-Focused Outcomes Patients Peer Review Performance Pharmaceutical Preparations Policies Population Predictive Cancer Model Preparation Recommendation Recording of previous events Research Personnel Risk Source Structure Terminology Time Training Unified Medical Language System Variant Work base cohort comorbidity Index coronavirus disease design diabetic patient experience flexibility improved knowledge integration model building predictive modeling public health relevance structured data symposium

项目摘要

Abstract In the era of big clinical data, the availability of rich real-world clinical data sources (RWcD) enables the development of predictive models for different clinical events, bringing the potential to improve efficiency and lower the cost of health care. However, the currently in-use models in practice are mostly trained on local data, introducing issues of bias and lack of generalizability. We will develop comprehensive methods to efficiently train high-quality clinical foundation model (CFM) that learn informative representations from patients' structured clinical data either in the form of EHR or claims. Specifically, how to train CFM that can maximize the performance boost for any downstream prediction tasks regardless of the predictive model architecture and the size of the available training data. In this application we propose to 1) Develop a flexible framework to intake the temporal structured clinical data elements from heterogenous sources and enrich it with existing knowledge, 2) Optimize the foundation model architecture and pre-training strategy, 3) Develop prompting strategies for zero/few shot learning, and 4) Evaluating CFM on multiple clinical downstream tasks.

抽象的在临床大数据时代，丰富的真实临床数据源（RWcD）的可用性使得开发不同临床事件的预测模型，有可能提高效率和降低医疗保健成本。然而，目前在实践中使用的模型大多是在本地数据上进行训练的，引入偏见和缺乏普遍性的问题。我们将制定综合方法，以高效训练高质量的临床基础模型 (CFM)，从患者的信息中学习信息表征 EHR 或索赔形式的结构化临床数据。具体来说，如何训练能够最大化的CFM 无论预测模型架构如何，任何下游预测任务的性能提升可用训练数据的大小。在此应用中，我们建议 1) 开发一个灵活的框架从异质来源获取时间结构化临床数据元素，并利用现有的内容对其进行丰富知识，2）优化基础模型架构和预训练策略，3）制定提示零/少样本学习策略，以及 4) 评估多个临床下游任务的 CFM。