A data science framework for transforming electronic health records into real-world evidence

将电子健康记录转化为现实世界证据的数据科学框架

基本信息

批准号：
10664706
负责人：
Vivek A Rudrapatna
金额：
$ 8.9万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
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：
2023-08-03 至 2025-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10664706
关键词：
3-Dimensional Acceleration Algorithms Bayesian Network Benchmarking Biometry California Chronic Classification Clinic Visits Clinical Data Clinical Research Clinical Trials Complex Data Data Reporting Data Science Data Set Dedications Disease Drug Approval E-learning Effectiveness Elderly Electronic Health Record Eligibility Determination Endoscopy Equity Exclusion Food and Drug Administration Drug Approval Future Goals Healthcare Immune System Diseases Joints Learning Machine Learning Malignant Neoplasms Masks Measurement Measures Mentors Methods Modeling Natural Language Processing Nature New Drug Approvals Patient Representative Patients Pattern Performance Pharmaceutical Preparations Population Predisposition Pregnancy Publishing Race Randomized, Controlled Trials Recording of previous events Research Subjects Sample Size San Francisco Selection for Treatments Semantics Severities Source Structure Subgroup Symptoms Testing Text Time Training Treatment Effectiveness Ulcerative Colitis Uncertainty Universities Work algorithm training career career development cohort computerized tools cost data harmonization data integration electronic structure heterogenous data improved in silico innovation insight learning strategy meetings outcome prediction patient health information patient subsets randomized trial reconstruction support tools tool treatment effect

项目摘要

PROJECT SUMMARY Randomized controlled trials (RCTs) are the gold-standard in clinical research but are subject to many limitations including high costs, limited generalizability, and small sample sizes in patient subgroups. By contrast, electronic health records (EHRs) are widely available and contain information on large and representative patient cohorts. However, because they capture the uncontrolled observations of many clinicians, they are highly susceptible to bias. The recent availability of the raw data from RCTs has created a unique opportunity to integrate them with that from EHRs, and to innovate methods that exploit the distinct advantages of each dataset. We propose to identify the zone of overlap between these data and build bridges in data representations. These bridges could enable us to better emulate randomized trials using EHR data and measure the same effects seen in the trials. Consequently, it would allow us to study subgroups that were excluded from the pivotal trials associated with new drug approvals by the FDA. We will test these ideas out in the context of Ulcerative colitis (UC) and scale to others in future work. We have obtained access to the raw data from 12 RCTs in UC (N=6,226). These data contain timed and structured measurements of disease activity including the Mayo score, a composite score of patient symptoms and endoscopic severity. We have also obtained access to the EHR data of 3,270 UC patients treated at the University of California San Francisco. These data contain similar data as RCTs but largely in an unstructured form. In addition, these assessments tend to be incomplete relative to trials due to costs and invasiveness of some tests. We will address this problem of unharmonized and incomplete EHR data in three aims. In Aim 1, we will harmonize the RCT data into an analysis-ready format. We will also develop text classification tools to transform free-texted EHR data into Mayo subscores, and validate these tools against data from a second center. In Aim 2, we will integrate the RCT and EHR data, train algorithms to impute RCT- based representations of the patient state from partial measurements made in EHRs, and test them under conditions typifying real-world data capture. In Aim 3, we will use these algorithms to harmonize EHR data, validate them as a tool to recover the same effects as RCTs, and study new patient subgroups. The applicant will carry out these aims and train in biostatistics, natural language processing, machine learning, and overall career development. With the help of his mentors, he will launch a career dedicated to developing and disseminating methods for learning from complex clinical data, and in so doing, promote a future of better healthcare for all patients.

项目摘要随机对照试验（RCT）是临床研究的金标准局限性包括高成本，有限的可推广性和患者亚组中的小样本量。经过对比，电子健康记录（EHR）广泛可用，并包含大型信息的信息代表性患者队列。但是，因为它们捕获了许多人的不受控制的观察结果临床医生，他们非常容易受到偏见的影响。来自RCT的原始数据的最新可用性创建了一个将它们与EHR相结合的独特机会，并创新利用独特的方法每个数据集的优点。我们建议确定这些数据之间的重叠区，并在数据表示中构建桥梁。这些桥梁可以使我们能够使用EHR数据更好地模拟随机试验并测量相同在试验中看到的效果。因此，这将使我们能够研究被排除在与FDA的新药批准相关的关键试验。我们将在溃疡性结肠炎（UC）的背景下测试这些想法，并在未来的工作中扩展到其他人。我们从UC中的12个RCT（n = 6,226）中获得了对原始数据的访问。这些数据包含定时和结构化疾病活动的测量，包括梅奥评分，患者症状的综合评分和内窥镜严重程度。我们还获得了3,270名在接受治疗的UC患者的EHR数据加利福尼亚大学旧金山大学。这些数据包含与RCT相似的数据，但主要在非结构化中形式。此外，由于成本和侵入性，这些评估往往是不完整的一些测试。我们将在三个目标中解决这个未经赎金和不完整的EHR数据的问题。在AIM 1中，我们将将RCT数据协调为分析就绪的格式。我们还将开发文字分类工具将自由文本的EHR数据转换为蛋黄酱，并验证这些工具反对来自第二个中心的数据。在AIM 2中，我们将集成RCT和EHR数据，训练算法以估算RCT- 通过在EHR中进行的部分测量对患者状态的基于患者状态的表示，并在下面进行测试代表现实世界数据捕获的条件。在AIM 3中，我们将使用这些算法来协调EHR数据，验证它们是一种恢复与RCT相同效果的工具，并研究新的患者子组。申请人将执行这些目标，并训练生物统计学，自然语言处理，机器学习和整体职业发展。在他的导师的帮助下，他将发起专门的职业开发和传播从复杂临床数据中学习的方法，并在这样做的过程中促进所有患者的医疗保健的未来。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

使用大型语言模型从临床记录中算法识别治疗中出现的不良事件：炎症性肠病的初步研究。

DOI：
10.1002/cpt.3226
发表时间：
2024
期刊：
Clinical pharmacology and therapeutics
影响因子：
6.7
作者：
Silverman,AnnaL;Sushil,Madhumita;Bhasuran,Balu;Ludwig,Dana;Buchanan,James;Racz,Rebecca;Parakala,Mahalakshmi;El-Kamary,Samer;Ahima,Ohenewaa;Belov,Artur;Choi,Lauren;Billings,Monisha;Li,Yan;Habal,Nadia;Liu,Qi;Tiwari,Jawahar;B
通讯作者：
B

Assessing the Impact of COVID-19 on IBD Outcomes Among Vulnerable Patient Populations in a Large Metropolitan Center.