Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques. We search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the developm...

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Veröffentlicht in:Journal of clinical epidemiology Jg. 154; S. 8 - 22
Hauptverfasser: Andaur Navarro, Constanza L., Damen, Johanna A.A., van Smeden, Maarten, Takada, Toshihiko, Nijman, Steven W.J., Dhiman, Paula, Ma, Jie, Collins, Gary S., Bajpai, Ram, Riley, Richard D., Moons, Karel G.M., Hooft, Lotty
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 01.02.2023
Elsevier Limited
Schlagworte:
Algorithms
Calibration
Datasets
Design
Development
Diagnosis
Epidemiology
Humans
Internal Medicine
Learning algorithms
Machine Learning
Medical diagnosis
Medical prognosis
Missing data
Patients
Prediction models
Predictive algorithm
Prognosis
Risk prediction
ROC Curve
Supervised learning
Supervised Machine Learning
Support vector machines
Systematic review
Validation
Validation
Development
Risk prediction
Prognosis
Diagnosis
Predictive algorithm
diagnosis
predictive algorithm
development
prognosis
risk prediction
validation
ISSN:0895-4356, 1878-5921, 1878-5921
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Zusammenfassung:We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques. We search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes. We included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]). Our review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models. PROSPERO, CRD42019161764.
Bibliographie:ObjectType-Article-2
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ObjectType-Evidence Based Healthcare-1
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ISSN:0895-4356
1878-5921
1878-5921
DOI:10.1016/j.jclinepi.2022.11.015