Recommendations for Reporting Machine Learning Analyses in Clinical Research
Open accessAbstract
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.
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Feature selection is best informed a priori by medical expert knowledge or existing literature.
“and is best informed - a priori - by medical expert knowledge or existing literature13,17,76.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
It has been widely recommended to report any measures taken to prevent overfitting.
“It has been widely recommended to report any measures taken to prevent overfitting3,7,8,13,15.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
Feature selection is a commonly referred strategy to prevent overfitting.
“One commonly referred strategy is feature selection13,15,27,47,76, for which it is explicitly recommended that selection should work independently of model training (unlike in methods as forward and backward selection) and is best informed - a priori - by medical expert knowledge or existing literature13,17,76.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
Feature selection should work independently of model training.
“for which it is explicitly recommended that selection should work independently of model training (unlike in methods as forward and backward selection)”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
To facilitate transparency and reproducibility, the computational architecture, high-performance techniques, software packages, and versioning should be reported.
“To facilitate transparency and reproducibility of the developmental process, the used computational architecture, high-performance techniques, software packages, and versioning (data, model, configurations and training scripts) should be reported13,14,16,50,64–66.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
External AIPM model validation can be based on discrimination, calibration, and Decision Curve Analysis.
“Similar to internal validation of the AIPM, external AIPM model validation can be based on discrimination (area under the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values), calibration (calibration plot)5,6,13,17,49,56,78, and Decision Curve Analysis5,6,17,49,68,72.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
Code for the complete model building pipeline should be published in well-documented scripts with computer environment requirements when possible.
“Code for the complete model building pipeline should be published in well-documented scripts with computer environment requirements when possible7,8,13,14,16,19,20,24,25,27,33,50,62,64,65, including statements about any restrictions to access or re-use.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
To ensure generalizability to the intended healthcare setting, developers are advised to extensively validate the model for representative data from that setting.
“To ensure the generalizability of the AIPM to the intended healthcare setting, developers are advised to extensively validate the model for representative data from that setting6–8,10,13–15,24,27,56,64,66,68,72,77,79,87,88 (see phase 1, Representativeness).”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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- Authors
- Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L., & Kao, D. P.
- Affiliations
- American Heart Association (US)
- Texas A&M University (US)
- Brigham and Women's Hospital (US)
- Duke University (US)
- University of Colorado Anschutz (US)
- Venue
- Circulation: Cardiovascular Quality and Outcomes
- Published
- 2020-10
- Volume
- 13
- Issue
- 10
- Pages
- e006556-e006556
- ISSN
- 1941-7705 (ISSN-L)
- 1941-7713
- PMC
- PMC8320533
- OpenAlex
- W3094171951
- Cited by
- 268
- References
- 34
- Citations by year
- 2026: 39 · 2025: 43 · 2024: 72 · 2023: 46 · 2022: 43 · 2021: 24 · 2020: 1
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Circulation Cardiovascular Quality and Outcomes (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K08HL125725, L30HL110124)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- machine learning, reporting guidelines, clinical research, reproducibility, transparency, peer review, manuscript template, model validation, cardiovascular research
- MeSH terms
- Data Accuracy, Machine Learning, Data Interpretation, Statistical, Humans, Periodicals as Topic, Research Design, Editorial Policies, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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