HCNN: Heterogeneous Convolutional Neural Networks for Comorbid Risk Prediction with Electronic Health Records

2017 ◽  
Author(s):  
Jinghe Zhang ◽  
Jiaqi Gong ◽  
Laura Barnes
Keyword(s):  
Neural Networks ◽  
Electronic Health Records ◽  
Risk Prediction ◽  
Convolutional Neural Networks ◽  
Health Records ◽  
Electronic Health

Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding

Drug Safety ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 113-122 ◽  
Author(s):  
Susmitha Wunnava ◽  
Xiao Qin ◽  
Tabassum Kakar ◽  
Cansu Sen ◽  
Elke A. Rundensteiner ◽  
...  
Keyword(s):  
Neural Networks ◽  
Electronic Health Records ◽  
Adverse Drug Event ◽  
Event Detection ◽  
Recurrent Neural Networks ◽  
Drug Event ◽  
Health Records ◽  
Electronic Health

scholarly journals A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis

2017 ◽  
Vol 36 (17) ◽  
pp. 2750-2763 ◽  
Author(s):  
Benjamin A. Goldstein ◽  
Gina Maria Pomann ◽  
Wolfgang C. Winkelmayer ◽  
Michael J. Pencina
Keyword(s):  
Electronic Health Records ◽  
Risk Prediction ◽  
Prediction Methods ◽  
Health Records ◽  
Repeated Observations ◽  
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.

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