Analysis of Machine Learning Techniques for Heart Failure Readmissions

By B. Mortazavi, Nicholas Downing, E. Bucholz, K. Dharmarajan, A. Manhapra, Shu-Xia Li, Sahand N. Negahban, Harlan M. Krumholz. Circulation. Cardiovascular Quality and Outcomes, 2016

336 citations recorded by the source corpus.

Background—The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Methods and Results—Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Conclusions—Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/fbc7cca4aba5c04a0540a291a45817fd29e6e41b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cardiology and Cardiovascular Medicine

Citations in the corpus

Artificial intelligence and machine learning in precision and genomic medicine (2021, background): “[170] performed research where they reported that machine learning might aid to predict thirty-day all-cause hospital readmission in heart failure patients.”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, background): “[40] Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, et al.”
Machine learning in cardiovascular medicine: are we there yet? (2018, background): “9 42 Here, we would like to high-light the conceptual limitations of readmission prediction and challenges with modelling approaches presented.”
Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, background): “Mortazavi et al.(27) Prediction of 30-day hospital readmission rates among patients with heart”
Artificial Intelligence in Medicine: Today and Tomorrow (2020, positive): “Applied to electronic patient records, AI has been used to predict the risk of cardiovascular disease, for instance acute coronary syndrome (22) and heart failure (23) better than traditional scales.”
Artificial Intelligence in Cardiology. (2018, positive): “This is a difficult problem where machine learning techniques have been shown to improve on traditional statistical methods (8,9).”