scholarly journals Development and Validation of Machine Learning Models: Electronic Health Record Data To Predict Visual Acuity After Cataract Surgery

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Stacey Alexeeff
Keyword(s):  
Machine Learning ◽  
Visual Acuity ◽  
Electronic Health Record ◽  
Cataract Surgery ◽  
Health Record ◽  
Learning Models ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Development And Validation ◽  
Machine Learning Models

scholarly journals Assessing stroke severity using electronic health record data: a machine learning approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Emily Kogan ◽  
Kathryn Twyman ◽  
Jesse Heap ◽  
Dejan Milentijevic ◽  
Jennifer H. Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Patient Outcomes ◽  
Stroke Severity ◽  
Health Record ◽  
Learning Models ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Machine Learning Models

Abstract Background Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structured real-world evidence databases seldom include the severity. The aim of this study was to use machine learning models to impute NIHSS scores for all patients with newly diagnosed stroke from multi-institution electronic health record (EHR) data. Methods NIHSS scores available in the Optum© de-identified Integrated Claims-Clinical dataset were extracted from physician notes by applying natural language processing (NLP) methods. The cohort analyzed in the study consists of the 7149 patients with an inpatient or emergency room diagnosis of ischemic stroke, hemorrhagic stroke, or transient ischemic attack and a corresponding NLP-extracted NIHSS score. A subset of these patients (n = 1033, 14%) were held out for independent validation of model performance and the remaining patients (n = 6116, 86%) were used for training the model. Several machine learning models were evaluated, and parameters optimized using cross-validation on the training set. The model with optimal performance, a random forest model, was ultimately evaluated on the holdout set. Results Leveraging machine learning we identified the main factors in electronic health record data for assessing stroke severity, including death within the same month as stroke occurrence, length of hospital stay following stroke occurrence, aphagia/dysphagia diagnosis, hemiplegia diagnosis, and whether a patient was discharged to home or self-care. Comparing the imputed NIHSS scores to the NLP-extracted NIHSS scores on the holdout data set yielded an R2 (coefficient of determination) of 0.57, an R (Pearson correlation coefficient) of 0.76, and a root-mean-squared error of 4.5. Conclusions Machine learning models built on EHR data can be used to determine proxies for stroke severity. This enables severity to be incorporated in studies of stroke patient outcomes using administrative and EHR databases.

scholarly journals Predicting Ventilator-Associated Pneumonia with Machine Learning

2020 ◽  
Author(s):  
Christine Giang ◽  
Jacob Calvert ◽  
Gina Barnes ◽  
Anna Siefkas ◽  
Abigail Green-Saxena ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Electronic Health Record ◽  
Health Record ◽  
Ventilator Associated Pneumonia ◽  
Learning Models ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Machine Learning Models

Abstract Objective Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. ​VAP diagnostics derived from machine learning methods that utilize electronic health record data have not yet been explored. The objective of this study is to compare the performance of a variety of machine learning models trained to predict whether VAP will be diagnosed during the patient stay.Methods A retrospective study examined data from 6,129 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different machine learning models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to area under the receiver operating characteristic curve (AUROC) on a 10% hold-out test set. Feature importance was measured in terms of Shapley values.Results The highest performing model achieved an AUROC value of 0.827. The most important features for the best-performing model were the length of time on mechanical ventilation, presence of antibiotics, sputum test frequency, and most recent Glasgow Coma Scale assessment.Discussion Supervised machine learning using patient electronic health record data is promising for VAP diagnosis and warrants further validation. Conclusion This tool has the potential to aid the timely diagnosis of VAP.

scholarly journals Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study

2019 ◽  
Vol 6 (10) ◽  
pp. e688-e695 ◽  
Author(s):  
Julia L Marcus ◽  
Leo B Hurley ◽  
Douglas S Krakower ◽  
Stacey Alexeeff ◽  
Michael J Silverberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Exposure Prophylaxis ◽  
Modelling Study
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