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Cited by 12Machine Learning in HealthcareArtificial Intelligence in HealthcareImbalanced Data Classification Techniques

CaliForest: calibrated random forest for health data

Open access
Park, Yubin, Ho, Joyce C.·2020·Proceedings of the ACM Conference on Health, Inference, and Learning
DOI 10.1145/3368555.3384461PMC PMC8299436
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Abstract

Real-world predictive models in healthcare should be evaluated in terms of discrimination, the ability to differentiate between high and low risk events, and calibration, or the accuracy of the risk estimates. Unfortunately, calibration is often neglected and only discrimination is analyzed. Calibration is crucial for personalized medicine as they play an increasing role in the decision making process. Since random forest is a popular model for many healthcare applications, we propose CaliForest, a new calibrated random forest. Unlike existing calibration methodologies, CaliForest utilizes the out-of-bag samples to avoid the explicit construction of a calibration set. We evaluated CaliForest on two risk prediction tasks obtained from the publicly-available MIMIC-III database. Evaluation on these binary prediction tasks demonstrates that CaliForest can achieve the same discriminative power as random forest while obtaining a better-calibrated model evaluated across six different metrics. CaliForest is published on the standard Python software repository and the code is openly available on Github.

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  • Some machine learning approaches might require additional development steps to ensure calibration.

    “Moreover, some machine learning approaches might require additional development steps to ensure calibration.94 121 122”

    From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [121]

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Research works printed but not linked: 4

  1. [6]Boström Henrik. 2008. Calibrating Random Forests. 2008 Seventh International Conference on Machine Learning and Applications (2008), 121– 126.
  2. [18]Nestor Bret, Matthew McDermott Willie Boag, Berner Gabriela, Naumann Tristan, Michael C Hughes Anna Goldenberg, and Ghassemi Marzyeh. 2019. Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks. arXiv preprint arXiv: 1908. 00690 (2019).
  3. [21]Platt John. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers 10, 3 (1999), 61– 74.
  4. [29]Wang Shirly, Matthew McDermott Geeticka Chauhan, Michael C Hughes Tristan Naumann, and Ghassemi Marzyeh. 2019. Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii. arXiv preprint arXiv: 1907. 08322 (2019).

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Authors
Park, Y., & Ho, J. C.
Affiliations
Emory University (US)
ORCID
Ho, Joyce C.
Venue
Proceedings of the ACM Conference on Health, Inference, and Learning
Publisher
ACM
Published
2020-04-02
Volume
2020
Pages
40-50
DOI
10.1145/3368555.3384461
PMC
PMC8299436
OpenAlex
W3011920305
Cited by
12
References
29
Citations by year
2026: 3 · 2025: 1 · 2024: 3 · 2023: 1 · 2021: 4
FWCI
0.48
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Conference on Health, Inference, and Learning (CHIL) (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
NIH (1K01LM012924-01)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
Keywords
probability calibration, risk prediction, discrimination (statistics), MIMIC-III, binary classification, personalized medicine

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