Optimal sampling for positive only electronic health record data.
Seong-Ho LeeYanyuan MaYing WeiJinbo ChenPublished in: Biometrics (2023)
Identifying a patient's disease/health status from electronic medical records is a frequently encountered task in electronic health records (EHR) related research, and estimation of a classification model often requires a benchmark training data with patients' known phenotype statuses. However, assessing a patient's phenotype is costly and labor intensive, hence a proper selection of EHR records as a training set is desired. We propose a procedure to tailor the best training subsample with limited sample size for a classification model, minimizing its mean-squared phenotyping/classification error (MSE). Our approach incorporates "positive only" information, an approximation of the true disease status without false alarm, when it is available. In addition, our sampling procedure is applicable for training a chosen classification model which can be misspecified. We provide theoretical justification on its optimality in terms of MSE. The performance gain from our method is illustrated through simulation and a real-data example, and is found often satisfactory under criteria beyond MSE.
Keyphrases
- electronic health record
- deep learning
- machine learning
- clinical decision support
- virtual reality
- adverse drug
- end stage renal disease
- case report
- minimally invasive
- chronic kidney disease
- newly diagnosed
- ejection fraction
- healthcare
- artificial intelligence
- high throughput
- patient reported outcomes
- health information
- single cell