title: 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 datadriven 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.
authors.affiliation: Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, New York, NY, USA2Institute of Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.
authors.forename: Khader
authors.initials: K
authors.lastname: Shameer
authors.forename: Kipp W
authors.initials: KW
authors.lastname: Johnson
authors.forename: Alexandre
authors.initials: A
authors.lastname: Yahi
authors.forename: Riccardo
authors.initials: R
authors.lastname: Miotto
authors.forename: L I
authors.initials: LI
authors.lastname: Li
authors.forename: Doran
authors.initials: D
authors.lastname: Ricks
authors.forename: Jebakumar
authors.initials: J
authors.lastname: Jebakaran
authors.forename: Patricia
authors.initials: P
authors.lastname: Kovatch
authors.forename: Partho P
authors.initials: PP
authors.lastname: Sengupta
authors.forename: Sengupta
authors.initials: S
authors.lastname: Gelijns
authors.forename: Alan
authors.initials: A
authors.lastname: Moskovitz
authors.forename: Bruce
authors.initials: B
authors.lastname: Darrow
authors.forename: David L
authors.initials: DL
authors.lastname: David
authors.forename: Andrew
authors.initials: A
authors.lastname: Kasarskis
authors.forename: Nicholas P
authors.initials: NP
authors.lastname: Tatonetti
authors.forename: Sean
authors.initials: S
authors.lastname: Pinney
authors.forename: Joel T
authors.initials: JT
authors.lastname: Dudley
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015331:Cohort Studies; D019295:Computational Biology; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006333:Heart Failure / Q000628:therapy; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D009519:New York City; D010359:Patient Readmission / Q000706:statistics & numerical data*