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Interpretable Deep Learning Model for Longitudinal Electronic Health Records and Applications to Heart Failure Prediction

用于纵向电子健康记录的可解释深度学习模型及其在心力衰竭预测中的应用

基本信息

批准号：
9544376
负责人：
Chao Zhang
金额：
$ 75.61万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-13 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9544376
关键词：
Accounting Address Admission activity Algorithms Attention Biological Neural Networks Caring Classification Clinical Code Complex Computer software Cost of Illness Data Decision Trees Detection Diagnosis Diagnostic Dimensions Disease Progression E-learning Early Diagnosis Electronic Health Record Event Future Health Health system Healthcare Heart failure Hospitals Image Imagery Individual Influentials Inpatients Institution Intuition Learning Logistic Regressions Measures Medical Methods Mission Modeling Natural Language Processing Neural Network Simulation Outcome Output Patient risk Patient-Focused Outcomes Patients Performance Pharmaceutical Preparations Phase Play Procedures Records Recurrence Research Risk Risk Factors Signs and Symptoms Software Tools Structure System Time Translating Work base clinical care clinical risk health application high dimensionality improved individual patient interest interoperability learning strategy mortality parallel computer patient stratification prediction algorithm predictive modeling relating to nervous system success

项目摘要

PROJECT SUMMARY Heart failure (HF) is a highly disabling and costly disease with a high mortality rate. In the prediagnostic phase (i.e., 1236 months before diagnosis), HF is difficult to detect given the insidious signs and symptoms. After diagnosis, where it is not possible to reverse disease progression, efforts are made to avoid hospital admission and readmission, but with limited capabilities to stratify patients by risk. We propose to develop interpretable deep learning models applied to largescale electronic health record (EHR) data to detect HF related events on two different time scales. One set of models will be developed to detect HF diagnosis one to two years before actual documented diagnosis. Separately, we propose to identify HF patients who are at risk of hospital admission and readmission . The project focuses on developing deep learning models that offer the potential for greater accuracy, clinical interpretability, and utility than alternatives. The expected deliverables include comprehensive software for creating deep learning algorithms that predict HF outcomes and related software tools for model visualization.

项目总结：心力衰竭(HF)是一种高度致残的疾病，也是一种代价高昂的疾病，具有极高的死亡率。目前处于诊断前阶段。 (即在确诊前12-36个月)，考虑到潜在的心脏症状和症状，心衰很难被检测出来。在诊断不可能逆转疾病进展的情况下，我们会尽最大努力避免住院。以及重新入院，但治疗能力有限的患者可以根据风险对患者进行分层。我们将提出建议，以制定一种可解释的解决方案。深度学习模型已应用于大规模医疗电子健康记录系统(EHR)的数据采集，以快速检测与心脏出血热相关的健康事件。两个不同的时间尺度。将不会开发一套新的模型，以在一到两年前检测心力衰竭的诊断。实际记录的是他的诊断。另外，我们将提出一种方法，以帮助识别那些面临医院风险风险的心衰患者。录取和再录取。该项目的重点是开发更深入的学习模式，为学生提供更大的潜在机会。与其他替代方案相比，更高的准确性、更好的临床可解释性、更高的实用性和更高的可交付性。用于创建能够预测心力衰竭结果的深度学习算法的全面的医疗软件以及相关的医疗软件。工具为模型和可视化提供了支持。

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）

Highly elevated polygenic risk scores are better predictors of myocardial infarction risk early in life than later.

DOI：
10.1186/s13073-021-00828-8
发表时间：
2021-01-28
期刊：
Genome medicine
影响因子：
12.3
作者：
Isgut M;Sun J;Quyyumi AA;Gibson G
通讯作者：
Gibson G

GRAM: Graph-based Attention Model for Healthcare Representation Learning.

DOI：
10.1145/3097983.3098126
发表时间：
2017-08
期刊：
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
影响因子：
0
作者：
Choi E;Bahadori MT;Song L;Stewart WF;Sun J
通讯作者：
Sun J

DDL: Deep Dictionary Learning for Predictive Phenotyping.