Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches

By Jarrod D. Frizzell, L. Liang, Phillip J. Schulte, C. Yancy, P. Heidenreich, Adrian F. Hernandez, Deepak L. Bhatt, G. Fonarow, W. Laskey. JAMA cardiology, 2017

316 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/26e90c98af3e36fb68cd0525758df9197b7b0f69

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

1. Gary S. Collins
2. B. Van calster
3. Evangelia Christodoulou
4. J. Verbakel
5. Benjamin S. Glicksberg
6. Joel T. Dudley
7. Ewout W. Steyerberg
8. Yuanfang Guan

In Cardiology and Cardiovascular Medicine

1. Gary S. Collins
2. Ewout W. Steyerberg
3. Jeroen J. Bax
4. James K. Min
5. Sameer Quazi

Citations in the corpus

• Machine learning in cardiovascular medicine: are we there yet? (2018, result): “41 As the authors themselves conclude, results of their large-scale machine learning attempt to predict readmissions are largely not concordant with both earlier and emerging findings.”
• Artificial Intelligence in Cardiology. (2018, background): “(13) found that machine learning algorithms were unable to predict 30-day readmission better than logistic regression.”
• A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, background): “[73] Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al.”
• Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, background): “Frizzell et al.(26) Prediction of 30-day readmission of patients with heart failure A tree-augmented naive”
• Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment (2020, result): “On the other hand, other studies reported that ML and conventional statistical methods have similar prognostic usefulness in predicting mortality in intensive care units [31], readmission in patients hospitalized for heart failure [32], and all-cause mortality and cardiovascular events [33].”