ArticleHealth care management science2015
Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.
Article in Health care management science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Pooled it
- Use of machine learning to analyse routinely collected intensive care unit data: a systematic review.Critical care (London, England) · 2019Pooled it
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- Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series.International journal of environmental research and public health · 2022Review
- Machine Learning Models for Predicting Mortality in 7472 Very Low Birth Weight Infants Using Data from a Nationwide Neonatal Network.Diagnostics (Basel, Switzerland) · 2022Article
- An Efficient and Effective Model to Handle Missing Data in Classification.BioMed research international · 2020Article
- Bayesian additive regression trees and the General BART model.Statistics in medicine · 2019Article
- ESTIMATING POPULATION AVERAGE CAUSAL EFFECTS IN THE PRESENCE OF NON-OVERLAP: THE EFFECT OF NATURAL GAS COMPRESSOR STATION EXPOSURE ON CANCER MORTALITY.The annals of applied statistics · 2019Article
- Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes.Health services research · 2018Article
- A comparison of rule-based and machine learning approaches for classifying patient portal messages.International journal of medical informatics · 2017Article
- Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- National Veterans Health Administration inpatient risk stratification models for hospital-acquired acute kidney injury.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Foreward to special issue on health analytics.Health care management science · 2015Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.