scholarly journals Deep Learning Based Data Governance for Chinese Electronic Health Record Analysis

2018 ◽  
Author(s):  
Junmei Zhong ◽  
Xiu Yi ◽  
Jian Wang ◽  
Zhuquan Shao ◽  
Panpan Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electronic Health Record ◽  
Health Record ◽  
Data Governance ◽  
Electronic Health

scholarly journals Early detection of sepsis utilizing deep learning on electronic health record event sequences

2020 ◽  
Vol 104 ◽  
pp. 101820 ◽  
Author(s):  
Simon Meyer Lauritsen ◽  
Mads Ellersgaard Kalør ◽  
Emil Lund Kongsgaard ◽  
Katrine Meyer Lauritsen ◽  
Marianne Johansson Jørgensen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Electronic Health Record ◽  
Health Record ◽  
Event Sequences ◽  
Electronic Health

scholarly journals The Use of Synthetic Electronic Health Record Data and Deep Learning to Improve Timing of High-Risk Heart Failure Surgical Intervention by Predicting Proximity to Catastrophic Decompensation

2020 ◽  
Vol 2 ◽  
Author(s):  
Aixia Guo ◽  
Randi E. Foraker ◽  
Robert M. MacGregor ◽  
Faraz M. Masood ◽  
Brian P. Cupps ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Deep Learning ◽  
High Risk ◽  
Electronic Health Record ◽  
Surgical Intervention ◽  
Health Record ◽  
Predictive Capability ◽  
Clinical Tool ◽  
Electronic Health

Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered the clinical threshold for application of high-risk surgical intervention, such as ventricular assist device placement. Machine learning can detect non-intuitive classifier patterns that allow for innovative combination of patient feature predictive capability. A machine learning-based clinical tool to identify proximity to catastrophic HF deterioration on a patient-specific basis would enable more efficient direction of high-risk surgical intervention to those patients who have the most to gain from it, while sparing others. Synthetic electronic health record (EHR) data are statistically indistinguishable from the original protected health information, and can be analyzed as if they were original data but without any privacy concerns. We demonstrate that synthetic EHR data can be easily accessed and analyzed and are amenable to machine learning analyses.Methods: We developed synthetic data from EHR data of 26,575 HF patients admitted to a single institution during the decade ending on 12/31/2018. Twenty-seven clinically-relevant features were synthesized and utilized in supervised deep learning and machine learning algorithms (i.e., deep neural networks [DNN], random forest [RF], and logistic regression [LR]) to explore their ability to predict 1-year mortality by five-fold cross validation methods. We conducted analyses leveraging features from prior to/at and after/at the time of HF diagnosis.Results: The area under the receiver operating curve (AUC) was used to evaluate the performance of the three models: the mean AUC was 0.80 for DNN, 0.72 for RF, and 0.74 for LR. Age, creatinine, body mass index, and blood pressure levels were especially important features in predicting death within 1-year among HF patients.Conclusions: Machine learning models have considerable potential to improve accuracy in mortality prediction, such that high-risk surgical intervention can be applied only in those patients who stand to benefit from it. Access to EHR-based synthetic data derivatives eliminates risk of exposure of EHR data, speeds time-to-insight, and facilitates data sharing. As more clinical, imaging, and contractile features with proven predictive capability are added to these models, the development of a clinical tool to assist in timing of intervention in surgical candidates may be possible.

scholarly journals Artificial Intelligence Based Data Governance for Chinese Electronic Health Record Analysis

2018 ◽  
Vol 8 (3) ◽  
pp. 29-41 ◽  
Author(s):  
Junmei Zhong ◽  
Xiu Yi ◽  
Jian Wang ◽  
Zhuquan Shao ◽  
Panpan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Health Record ◽  
Data Governance ◽  
Electronic Health

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

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