Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort

Abstract

Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6–0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such data-driven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

Machine learning in cardiovascular medicine: are we there yet?

“…These data are generally used to develop a model to predict or classify future events, or to find which variables are most relevant to the outcome. Examples of supervised learning algorithms include ordinary least squares regression,18 logistic regression,19 least absolute shrinkage and selection operator (LASSO) regression,20 ridge regression,21 elastic net regression,21 linear discriminant analysis,22 Naïve Bayes classifiers,9 support vector machines,23 Bayesian networks,24 a variety of decision trees25 especially Random Forests26 and AdaBoost or gradient boosting classifiers,27 artificial neural networks and ensemble methods 7. Some of the examples of supervised machine learning tasks include regression, classification, predictive modelling and survival analysis.…”

Predicting common maternal postpartum complications: leveraging health administrative data and machine learning

“…Recent studies have used administrative data with various machine learning techniques to predict hospital re‐admission for selected disorders after discharge . However, no studies have utilised administrative data sources to build predictive models to estimate the risk of postpartum complications.…”

“…5 Recent studies have used administrative data with various machine learning techniques to predict hospital re-admission for selected disorders after discharge. [6][7][8][9] However, no studies have utilised administrative data sources to build predictive models to estimate the risk of postpartum complications. We investigated the potential to develop predictive models to provide timely and accurate information to inform immediate postpartum and post-discharge care among the entire population of pregnant women.…”

Modelling hospital readmissions under frailty conditions for healthy aging

“…Recent works favor the application of predictive machine‐learning approaches, formulating readmission prediction as a binary classification problem (Artetxe et al, ; Ottenbacher et al, ). Instances of classifier models used for readmission prediction are support vector machines (SVM) (Besga et al, ; Futoma et al, ; Zheng et al, ; Cui et al, ) deep learning (Reddy & Delen, ; Xiao et al, ), artificial neural network (Ottenbacher et al, ), and naïve Bayes (Vukicevic et al, ; Shameer et al, ).…”

“…Readmission prediction in the case of adult patients has been tackled with diverse statistical approaches (Artetxe et al, 2018;Kansagara et al, 2011) such as logistic regression (Garmendia et al, 2017;Ottenbacher et al, 2001) and survival analysis (Garmendia et al, 2019). machines (SVM) (Besga et al, 2015;Futoma et al, 2015;Zheng et al, 2015;Cui et al, 2018) deep learning (Reddy & Delen, 2018;Xiao et al, 2018), artificial neural network (Ottenbacher et al, 2001), and naïve Bayes (Vukicevic et al, 2015;Shameer et al, 2017).…”