Predicting who will fracture: Exploration of machine learning in the observational Women's Health Initiative Study dataset.

项目摘要 所有绝经后妇女中有一半将在其剩余的生命中经历与骨质疏松症相关的骨折。 由于这些骨折可能导致残疾、丧失独立性和死亡，因此确定哪些人处于风险中非常重要 进行早期干预和缓解。虽然临床指南支持对女性进行常规骨质疏松症筛查， 年龄≥65岁，仅建议对年龄50-64岁的绝经后年轻女性进行选择性筛查 基于风险评估工具的使用（例如，OST、FRAX、SCORE）。然而，我们已经表明，这些 工具-不是专门为这个年龄组的妇女开发的-不能很好地区分 患有和未患有骨质疏松症（基于骨矿物质密度，BMD）和/或随后骨折的女性。 本项目的目的是探索机器学习（ML），以改善骨质疏松症的风险评估， 年轻的绝经后妇女。既往对骨质疏松症和相关骨折进行了基于ML的分析， 非美国人口和/或规模有限。我们将使用大型妇女健康倡议（WHI）研究 （来自美国的超过160，000人），以开发，验证和比较不同的机器学习 针对年轻绝经后的方法（随机森林;逻辑回归;动态信念网络，DBN） 妇女ML模型将被构建和评估用于两个任务：1）预测老年女性的骨折风险 50-64（目标1）;和2）预测骨质疏松症（根据BMD;目标2）。在每种情况下，我们将使用 现有的风险因素，以及从WHI收集的额外变量，以确定新的 可以提高预测能力的功能。我们还将通过构建DBN来评估时态模型的价值， 利用个人过去的观察来指导预测。我们将计算技术性能指标 (e.g.,敏感性、特异性、阳性预测值），并进行误差分析以对比哪些（亚）组 每个模型（in）正确地识别。我们亦会进行敏感度分析，以确定不同 变量对模型预测的鲁棒性的影响。最后，我们计划对模型进行外部验证（目标3 从目标1和2使用电子健康记录（EHR）数据集从加州大学洛杉矶分校和加州大学旧金山分校，调查程度 可运输性。R21的成功执行将：1）开发和测试不同的ML模型，预测主要的 美国女性中的骨质疏松性骨折和骨质疏松症; 2）确定告知风险的潜在其他变量 这些条件;和3）提供洞察力的领域，这种ML模型可以通过分层改进， 阳离子和/或未来的方法。该R21的结果将作为更广泛的 R 01开发更有效的骨折和骨关节炎风险预测模型。

PROJECT ABSTRACT Half of all postmenopausal women will experience an osteoporosis-related fracture in their remaining lifetimes. As these fractures can lead to disability, loss of independence, and death, it is important to identify who is at risk for early intervention and mitigation. While clinical guidelines support routine osteoporosis screening for women aged ≥65 years, only selective screening is recommended for younger postmenopausal women aged 50-64 based on the use of risk assessment tools (e.g., OST, FRAX, SCORE). However, we have shown that these tools – which were not specifically developed for women in this age group – do not differentiate well between women who do and do not have osteoporosis (based on bone mineral density, BMD) and/or subsequent fracture. The objective of this project is to explore machine learning (ML) to improve osteoporosis risk assessment in young postmenopausal women. Prior ML-based analyses for osteoporosis and related fractures exist but are on non-American populations and/or are of limited size. We will use the large Women's Health Initiative (WHI) Study (>160,000 individuals from the United States), to develop, validate, and compare different machine learning approaches (random forests; logistic regression; dynamic belief network, DBN) for younger postmenopausal women. ML models will be constructed and assessed for two tasks: 1) predicting fracture risk in women aged 50-64 (Aim 1); and 2) predicting osteoporosis (per BMD; Aim 2). In each case, we will build ML models using existing risk factors from current tools, as well as add additional variables collected from the WHI to identify new features that may improve predictive power. We will also assess the value of temporal model by building DBNs, using an individual's past observations to guide predictions. We will compute technical performance metrics (e.g., sensitivity, specificity, positive predictive value) and conduct error analyses to contrast what (sub)groups each model (in)correctly identifies. We will also perform sensitivity analyses to ascertain the impact of different variables on the robustness of the model's predictions. Lastly, we plan to externally validate (Aim 3) the models from Aims 1 & 2 using electronic health record (EHR) datasets from UCLA and UCSF, investigating the degree of transportability. Successful execution of this R21 will: 1) develop and test different ML models predicting major osteoporotic fracture and osteoporosis in US women; 2) identify potential additional variables that inform the risk of these conditions; and 3) provide insight into areas where such ML-models may be improved through stratifi- cation and/or future methodological approaches. The results from this R21 will serve as a baseline for a broader R01 to develop more effective predictive models for fracture and osteoporotic risk.