Machine-learning-based patient-specific prediction models for knee osteoarthritis
Machine-learning-based patient-specific prediction models for knee osteoarthritis
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DOI：
10.1038/s41584-018-0130-5
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发表时间：
2019-01-01
影响因子：
33.7
通讯作者：
Martel-Pelletier, Johanne
中科院分区：
文献类型：
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作者：
Jamshidi, Afshin;Pelletier, Jean-Pierre;Martel-Pelletier, Johanne
骨关节炎（OA）是一种极为常见的肌肉骨骼疾病。然而，目前的指南不太适合诊断疾病早期阶段的患者，并且不区分疾病可能快速进展的患者。OA管理中最重要的障碍是识别和分类将从治疗中获益最多的患者。在患者分组和开发预测模型方面需要进一步努力。存在传统的统计建模方法;然而，这些模型在它们能够充分处理的信息量方面是有限的。需要开发全面的患者特异性预测模型。数据挖掘和机器学习等方法应有助于开发此类模型。虽然这是一项具有挑战性的任务，但现在已有技术可以对OA患者进行分组，并改善临床决策和精准医疗。
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.