ReviewInternational journal of environmental research and public health2022
Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series.
Review in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 38 citations in OpenAlex.
- Article
- Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.Annals of surgical oncology · 2026Article
- Machine learning prediction of long-term postoperative pneumonia risk: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Can machine learning support infection control measures by predicting carbapenemase-producing Enterobacterales colonization at admission?Infection control and hospital epidemiology · 2026Article
- Integrating Host Genetics and Clinical Setting in Machine Learning Models: Predicting COVID-19 Prognosis for Healthcare Decision-Making (The FeMiNa Study).Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning algorithms for predicting glycemic control and weight loss outcomes in GLP-1 receptor agonist users.Frontiers in artificial intelligence · 2026Article
- Machine learning studies of drug-induced nephrotoxicity: a scoping review.Therapeutic advances in drug safety · 2026Article
- Using tree-based ensemble methods to produce a population-based mortality risk score in Ontario, Canada.PloS one · 2026Article
- Development of a prediction method for severe pancreatitis using a nomogram.Frontiers in medicine · 2026Article
- Tailoring Bayesian Additive Regression Trees (BART) for environmental mixture studies.PloS one · 2026Article
- Artificial intelligence-driven risk prediction of polypharmacy in older adults: current advances, clinical applications, and future perspectives.Frontiers in public health · 2026Review
- Comparing the use of supervised machine learning variable selection methods in the context of two-group classification in the psychological and health sciences.Frontiers in psychology · 2026Article
- A multidimensional analysis of the 21Scientific reports · 2025Article
- Unraveling the drivers of leptospirosis risk in Thailand using machine learning.PLoS neglected tropical diseases · 2025Article
- Predicting mechanical complications in adult spinal deformity patients with postoperative proportioned and moderately disproportioned alignment.Acta orthopaedica et traumatologica turcica · 2025Article
- Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data.Scientific reports · 2025Article
- COMPARATIVE EFFECTIVENESS OF PROPENSITY SCORE ESTIMATION METHODS FOR INVERSE PROBABILITY OF TREATMENT WEIGHTING ANALYSIS WITH COMPLEX SURVEY DATA: A SIMULATION STUDY.Journal of survey statistics and methodology · 2025Article
- Dementia classification using two-channel electroencephalography features.Scientific reports · 2025Article
- Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.Biomolecules & biomedicine · 2025Article
- Application of machine learning in breast cancer survival prediction using a multimethod approach.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Tree-based machine learning methods have gained traction in the statistical and data science fields. They have been shown to provide better solutions to various research questions than traditional analysis approaches. To encourage the uptake of tree-based methods in health research, we review the methodological fundamentals of three key tree-based machine learning methods: random forests, extreme gradient boosting and Bayesian additive regression trees. We further conduct a series of case studies to illustrate how these methods can be properly used to solve important health research problems in four domains: variable selection, estimation of causal effects, propensity score weighting and missing data. We exposit that the central idea of using ensemble tree methods for these research questions is accurate prediction via flexible modeling. We applied ensemble trees methods to select important predictors for the presence of postoperative respiratory complication among early stage lung cancer patients with resectable tumors. We then demonstrated how to use these methods to estimate the causal effects of popular surgical approaches on postoperative respiratory complications among lung cancer patients. Using the same data, we further implemented the methods to accurately estimate the inverse probability weights for a propensity score analysis of the comparative effectiveness of the surgical approaches. Finally, we demonstrated how random forests can be used to impute missing data using the Study of Women's Health Across the Nation data set. To conclude, the tree-based methods are a flexible tool and should be properly used for health investigations.
Indexed as
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What OpenQuestion holds
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.