A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records

2018 ◽  
Vol 65 (5) ◽  
pp. 956-968 ◽  
Author(s):  
Zhengxing Huang ◽  
Wei Dong ◽  
Huilong Duan ◽  
Jiquan Liu
Keyword(s):  
Acute Coronary Syndrome ◽  
Deep Learning ◽  
Electronic Health Records ◽  
Risk Prediction ◽  
Learning Approach ◽  
Health Records ◽  
Coronary Syndrome ◽  
Clinical Risk ◽  
Electronic Health

Relational regularized risk prediction of acute coronary syndrome using electronic health records

2018 ◽  
Vol 465 ◽  
pp. 118-129 ◽  
Author(s):  
Zhengxing Huang ◽  
Zhenxiao Ge ◽  
Wei Dong ◽  
Kunlun He ◽  
Huilong Duan ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Electronic Health Records ◽  
Risk Prediction ◽  
Health Records ◽  
Coronary Syndrome ◽  
Electronic Health

scholarly journals Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

2016 ◽  
Vol 24 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Benjamin A Goldstein ◽  
Ann Marie Navar ◽  
Michael J Pencina ◽  
John P A Ioannidis
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Risk Prediction ◽  
Model Building ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Health Records ◽  
Clinical Risk ◽  
Electronic Health ◽  
And Performance

Objective: Electronic health records (EHRs) are an increasingly common data source for clinical risk prediction, presenting both unique analytic opportunities and challenges. We sought to evaluate the current state of EHR based risk prediction modeling through a systematic review of clinical prediction studies using EHR data. Methods: We searched PubMed for articles that reported on the use of an EHR to develop a risk prediction model from 2009 to 2014. Articles were extracted by two reviewers, and we abstracted information on study design, use of EHR data, model building, and performance from each publication and supplementary documentation. Results: We identified 107 articles from 15 different countries. Studies were generally very large (median sample size = 26 100) and utilized a diverse array of predictors. Most used validation techniques (n = 94 of 107) and reported model coefficients for reproducibility (n = 83). However, studies did not fully leverage the breadth of EHR data, as they uncommonly used longitudinal information (n = 37) and employed relatively few predictor variables (median = 27 variables). Less than half of the studies were multicenter (n = 50) and only 26 performed validation across sites. Many studies did not fully address biases of EHR data such as missing data or loss to follow-up. Average c-statistics for different outcomes were: mortality (0.84), clinical prediction (0.83), hospitalization (0.71), and service utilization (0.71). Conclusions: EHR data present both opportunities and challenges for clinical risk prediction. There is room for improvement in designing such studies.

scholarly journals Adoption of clinical risk prediction tools is limited by a lack of integration with electronic health records

2021 ◽  
Vol 28 (1) ◽  
pp. e100253
Author(s):  
Videha Sharma ◽  
Ibrahim Ali ◽  
Sabine van der Veer ◽  
Glen Martin ◽  
John Ainsworth ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Risk Prediction ◽  
Health Records ◽  
Prediction Tools ◽  
Clinical Risk ◽  
Electronic Health
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