Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics.
Luke S FarrowMingjun ZhongGeorge Patrick AshcroftLesley AndersonR M Dominic MeekPublished in: The bone & joint journal (2021)
There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article: Bone Joint J 2021;103-B(12):1754-1758.
Keyphrases
- machine learning
- artificial intelligence
- big data
- healthcare
- adverse drug
- public health
- deep learning
- minimally invasive
- primary care
- trauma patients
- mental health
- electronic health record
- single cell
- quality improvement
- emergency department
- systematic review
- coronary artery bypass
- clinical practice
- human health
- health information
- acute coronary syndrome
- coronary artery disease