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Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods
Jaime L Speiser1, Wesley T Kerr1, Andreas Ziegler1
1From the Department of Biostatistics and Data Science (J.L.S.), Wake Forest University School of Medicine, Winston-Salem, NC; Department of Neurology and Biomedical Informatics (W.T.K.), University of Pittsburgh, PA; Cardio-CARE (A.Z.), Medizincampus Davos, Switzerland; Department of Cardiology and Population Health Innovation (A.Z.), University Medical Center Hamburg-Eppendorf, Germany; and Department of Mathematics, Statistics and Computer Science (A.Z.), University of KwaZulu-Natal, Berea, Durban, South Africa.
Researchers highlight common critiques in machine learning (ML) studies within neurology. Following reporting guidelines and involving experts can improve the quality and rigor of ML research in this field.
Area of Science:
- Neurology
- Biostatistics
- Machine Learning
Background:
- Machine learning (ML) methods are increasingly used in neurology research as alternatives to traditional statistical approaches for analyzing complex datasets.
- Despite their growing popularity, many ML studies in neurology do not fully adhere to recommended reporting guidelines, leading to common critiques.
Purpose of the Study:
- To identify and detail common critiques encountered in machine learning research studies within the field of neurology.
- To provide actionable recommendations for authors to avoid these critiques, thereby enhancing the quality and rigor of ML research reporting.
Main Methods:
- Review of common critiques in machine learning studies by editorial board members with expertise in neurology and ML.
- Categorization of critiques related to study goals, ML terminology, study design (sample size, data suitability, analysis pipeline, missing data handling, uncertainty estimates), and reporting of strengths/limitations.
- Provision of neurology-specific examples and guidance for avoiding identified critiques.
Main Results:
- Seven common critiques were identified, including misalignment of study goals and analysis, incorrect ML terminology, insufficient justification of sample size and data suitability, inadequate description of the ML pipeline, poor handling of missing data, lack of uncertainty estimates, and imbalanced reporting of study strengths and limitations.
- Recommendations include utilizing ML-specific reporting checklists and involving statisticians and ML experts in study design and execution.
Conclusions:
- Adherence to reporting guidelines and careful methodological execution are crucial for improving the quality and interpretability of machine learning studies in neurology.
- Implementing the provided recommendations can help researchers avoid common pitfalls, enabling a more thorough evaluation of ML approaches in neurological research.
- Machine learning holds significant potential for advancing neurology, contingent upon rigorous study design and transparent reporting of methods and results.
