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Machine-learning-based patient-specific prediction models for knee osteoarthritis
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DOI：10.1038/s41584-018-0130-5.png)
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Abstract
En 中文
Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process. Comprehensive patient-specific prediction models need to be developed. Approaches such as data mining and machine learning should aid in the development of such models. Although a challenging task, technology is now available that should enable subgrouping of patients with OA and lead to improved clinical decision-making and precision medicine.
Keywords:
CARTILAGE VOLUME LOSS
RISK PREDICTION
BREAST-CANCER
JOINT REPLACEMENT
BIG DATA
VALIDATION
CLASSIFICATION
PROGRESSION
CHALLENGES
IMPUTATION
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