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| 骨质疏松性骨折风险预测模型研究进展 |
| Research progress in the risk prediction models for osteoporotic fracture |
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| DOI：10.3969/j.issn.1006-7108.2024.10.020 |
| 中文关键词: 骨质疏松症 骨质疏松性骨折 早期诊断（识别） 机器学习 预测模型 |
| 英文关键词:osteoporosis osteoporotic fracture early diagnosis (identification) machine learning prediction models |
| 基金项目:国家自然科学基金面上项目（82074458）；江苏省自然科学基金面上项目（BK20221351）；江苏省自然科学基金青年项目（BK20220470）；江苏省高等学校自然科学研究面上项目（22KJB360012）；江苏省卫生健康发展研究中心开放课题（JSHD2021027）；国家中医药管理局高水平中医药重点学科建设项目资助（国中医药人教函〔2023〕85号）；江苏省中医退行性骨关节病临床医学创新中心资助项目(苏中医科教〔2021〕4号） |
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| 中文摘要: |
| 骨质疏松性骨折是骨质疏松症的严重后果，早期识别骨折高危风险，采取个性化的诊疗方案，进而降低骨折风险尤为重要。预测模型能够对骨折风险进行分层预测，对骨折的预后也具有重要意义。骨质疏松性骨折风险预测模型的研究数量众多、方法不一，本文从模型构建方法的角度出发，对比传统统计学方法与机器学习算法构建预测模型的优劣，总结分析骨质疏松性骨折预测模型的研究现状，以期为临床医师决策提供有益参考。 |
| 英文摘要: |
| Osteoporotic fractures are serious consequences of osteoporosis. Early identification of high fracture risks and the adoption of personalized diagnosis and treatment plans are particularly crucial in reducing the risk of fractures. Predictive models are capable of stratifying fracture risks and hold significant importance in predicting fracture outcomes. There is a plethora of research on predictive models for osteoporotic fracture risks, employing varied methodologies. This article focuses on comparing the advantages and disadvantages of constructing predictive models using traditional statistical methods versus machine learning algorithms from the perspective of model construction methods. It aims to summarize and analyze the current research status of predictive models for osteoporotic fractures, intending to provide valuable insights for clinical decision-making among healthcare professionals. |
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