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Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.
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.
Afiliación
  • Andaur Navarro CL; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. Electronic address: c.l.andaurnavarro@umcutrecht.nl.
  • Damen JAA; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • van Smeden M; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Takada T; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Nijman SWJ; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Dhiman P; Center for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Ma J; Center for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK.
  • Collins GS; Center for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Bajpai R; Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK.
  • Riley RD; Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK.
  • Moons KGM; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Hooft L; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
J Clin Epidemiol ; 154: 8-22, 2023 02.
Article en En | MEDLINE | ID: mdl-36436815
BACKGROUND AND OBJECTIVES: We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques. METHODS: 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. RESULTS: 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]). CONCLUSION: 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. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD42019161764.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Aprendizaje Automático Supervisado Tipo de estudio: Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Aprendizaje Automático Supervisado Tipo de estudio: Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos