Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk Prediction

用于风险预测的电子健康记录数据去噪半监督方法

• 批准号: 10453558
• 负责人: TIANXI CAI
• 金额: $ 33.74万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10453558
• 关键词: Address; Adoption; Aftercare; Algorithms; Biological; Clinical; Clinical Data; Clinical Investigator; Clinical Trials; Code; Cohort Studies; Colon Carcinoma; Colorectal Cancer; Complex; Computer software; Computerized Medical Record; Data; Data Set; Development; Dimensions; Disease; Disease Outcome; Disease Progression; Electronic Health Record; Face; Factor X; Genes; Goals; Gold; Healthcare; Healthcare Systems; Heterogeneity; Human; Inflammatory; Inflammatory Bowel Diseases; Label; Learning; Malignant Neoplasms; Massachusetts; Measurement; Medical Genetics; Medical Records; Meta-Analysis; Methodology; Methods; Modeling; Oncology; Outcome; Patients; Population; Procedures; Registries; Research; Research Activity; Research Proposals; Risk; Risk Estimate; Risk Factors; Site; Source; Specimen; Statistical Algorithm; Statistical Methods; Supervision; System; TNF gene; Target Populations; Testing; Training; Translational Research; Validation; adalimumab; antagonist; base; biobank; burden of illness; clinical application; clinical practice; cohort; colon cancer risk; cost; cost effective; deep learning; denoising; disorder risk; electronic data; genomic data; high dimensionality; improved; individualized medicine; infliximab; large datasets; learning strategy; machine learning method; machine learning model; multiple data sources; novel; outcome prediction; patient population; patient privacy; personalized management; personalized predictions; personalized risk prediction; precision medicine; predictive modeling; programs; repository; risk prediction; risk prediction model; study population; supervised learning; tool; transfer learning; treatment response; user-friendly

Project Summary While clinical trials remain a critical source for oncology research, their study findings may not be gener- alizable to the real world due to the restricted patient population. In recent years, due to the increasing adoption of electronic health records (EHR) and the linkage of EHR with specimen bio-repositories and other research registries, integrated large datasets now exist as a new source for translational research. These integrated datasets open opportunities for developing accurate EHR-based prediction models for disease progression and treatment response, which can be easily incorporated into clinical practice. These models can also be contrasted with models derived from clinical trials, bridging the gap between clinical trials and the real world. However, efficiently deriving and evaluating personalized prediction models using such real world data (RWD) remains challenging due to practical and methodological obstacles. For example, validated outcome information from EHR, such as development of colon cancer and 1-year treatment response, requires laborious medical record review and hence is often not readily available for research. Naive use of error prone surrogates of the outcome, such as billing codes or procedure codes, as the true outcome may greatly hamper the power of EHR studies and produce biased results. Semi-supervised risk prediction methods, leveraging noisy surrogates and a small amount of human annotations on the outcome, may greatly improve the utility of EHR for precision medicine research. Deriving a precise estimate of the risk model becomes even more challenging when the number of candidate features is not small relative to the number of annotated outcomes. Another major challenge with EHR risk modeling lies in the transportability. Complex machine learning models trained in one EHR system often attain low accuracy in another EHR system, due to the heterogeneity in the patient population and healthcare system. Transfer learning methods that can automatically adjust model developed for one EHR cohort to better fit to another EHR cohort is of great value. Synthesizing information from multiple data sources can improve the quality of evidence. However, meta analyzing EHR from multiple EHR cohorts faces an additional challenge due to patient privacy. We address these challenges by developing semi-supervised risk prediction methods with high dimensional predictions in Aim 1; semi-supervised transfer learning methods to enable risk prediction modeling in target populations with no gold standard labels uted learin Aim 2; and distributed learning methods for high dimensional predictive modeling in Aim.

项目摘要 虽然临床试验仍然是肿瘤学研究的关键来源，但他们的研究结果可能不会更通用- 由于有限的病人数量，可与现实世界相适应。近年来，由于越来越多的人采用 电子健康记录(EHR)以及电子健康记录与标本生物库和其他研究的联系 登记处、综合大数据集现在作为翻译研究的新来源而存在。这些集成在一起 数据集为开发准确的基于EHR的疾病进展预测模型提供了机会 和治疗反应，这可以很容易地纳入临床实践。这些型号还可以 与来自临床试验的模型形成对比，弥合了临床试验与现实世界之间的差距。 然而，使用这样的真实世界数据(RWD)高效地导出和评估个性化预测模型 由于实践和方法上的障碍，仍然具有挑战性。例如，经过验证的结果 来自EHR的信息，如结肠癌的发展和一年的治疗反应，需要 进行繁重的病历审查，因此往往不容易进行研究。天真地使用容易出错的 替代结果，例如帐单代码或程序代码，因为真实结果可能会极大地阻碍 电子病历的力量研究和产生有偏见的结果。半监督风险预测方法，杠杆 嘈杂的代孕和少量的人类对结果的注释，可能会大大提高实用性 精准医学研究的电子病历。得出风险模型的精确估计就变得更加困难了 当候选特征的数量相对于带注释的结果的数量不小时具有挑战性。 EHR风险建模的另一个主要挑战在于可移植性。复杂机器学习模型 在一个EHR系统中训练的准确率通常在另一个EHR系统中较低，这是由于 患者群体和医疗保健系统。传递可自动调整模型的学习方法 为一个电子病历队列开发，以更好地适应另一个电子病历队列具有重要价值。综合信息 从多个数据来源可以提高证据的质量。然而，Meta从多个角度分析了EHR 由于患者隐私的原因，EHR队列面临着额外的挑战。我们通过发展 目标1中高维预测的半监督风险预测方法；半监督转移 在没有金标标签的目标人群中实现风险预测建模的学习方法 AIM中高维预测建模的分布式学习方法。