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This summary is machine-generated.

Machine learning (ML) offers clinical research powerful predictive capabilities. This study recommends clear reporting guidelines for ML analyses to improve reproducibility and critical evaluation by clinicians and researchers.

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bioinformaticsmachine learningprognosisreportreproducibilityresearch

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Area of Science:

  • Clinical research methodology
  • Biostatistics
  • Computational biology

Background:

  • Machine learning (ML) is increasingly used in clinical research due to complex data availability.
  • ML offers advantages in predictive performance and identifying patient subpopulations.
  • Lack of familiarity with ML analysis evaluation hinders accurate interpretation by clinicians and peer reviewers.

Purpose of the Study:

  • To address the need for clear and structured reporting of ML analyses in clinical research.
  • To provide guidance for clinicians and researchers on evaluating ML models.
  • To improve the reproducibility and credibility of ML-based studies.

Main Methods:

  • Development of a recommendation for transparent and structured reporting of ML analysis results.
  • Creation of a list of key reporting elements with examples.
  • Targeting the specific needs of clinical researchers and manuscript preparation.

Main Results:

  • Proposed a framework for reporting ML analyses tailored for a clinical audience.
  • Provided a template of essential reporting elements for ML manuscripts.
  • Aimed to enhance the understanding and critical appraisal of ML models in clinical research.

Conclusions:

  • Clear, concise, and structured reporting is essential for ML in clinical research.
  • The presented recommendations and template can improve the quality and interpretability of ML studies.
  • Facilitating critical evaluation of ML models enhances their reliable application in clinical practice.