Premium
Machine‐learning algorithms for predicting hospital re‐admissions in sickle cell disease
Author(s) -
Patel Arisha,
Gan Kyra,
Li Andrew A.,
Weiss Jeremy,
Nouraie Mehdi,
Tayur Sridhar,
Novelli Enrico M.
Publication year - 2021
Publication title -
british journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.907
H-Index - 186
eISSN - 1365-2141
pISSN - 0007-1048
DOI - 10.1111/bjh.17107
Subject(s) - medicine , confidence interval , logistic regression , statistic , algorithm , machine learning , random forest , retrospective cohort study , emergency medicine , statistics , computer science , mathematics
Summary Reducing preventable hospital re‐admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine‐Learning (ML) algorithms may outperform standard re‐admission scoring systems (LACE and HOSPITAL indices). Participants ( n = 446) included patients with SCD with at least one unplanned inpatient encounter between January 1, 2013, and November 1, 2018. Patients were randomly partitioned into training and testing groups. Unplanned hospital admissions ( n = 3299) were stratified to training and testing samples. Potential predictors ( n = 486), measured from the last unplanned inpatient discharge to the current unplanned inpatient visit, were obtained via both data‐driven methods and clinical knowledge. Three standard ML algorithms, Logistic Regression (LR), Support‐Vector Machine (SVM), and Random Forest (RF) were applied. Prediction performance was assessed using the C ‐statistic, sensitivity, and specificity. In addition, we reported the most important predictors in our best models. In this dataset, ML algorithms outperformed LACE [ C ‐statistic 0·6, 95% Confidence Interval (CI) 0·57–0·64] and HOSPITAL ( C ‐statistic 0·69, 95% CI 0·66–0·72), with the RF ( C ‐statistic 0·77, 95% CI 0·73–0·79) and LR ( C ‐statistic 0·77, 95% CI 0·73–0·8) performing the best. ML algorithms can be powerful tools in predicting re‐admission in high‐risk patient groups.