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

项目摘要 一半的绝经后妇女将在其余生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，因此确定谁处于危险之中是很重要的 进行早期干预和缓解。虽然临床指南支持对女性进行常规的骨质疏松筛查 Agee≥65岁，建议50岁更年轻的绝经后女性仅进行选择性筛查-- 基于风险评估工具(例如，OST、FRAX、SCORE)的使用。然而，我们已经证明，这些 工具--不是专门为这个年龄段的女性开发的--无法很好地区分 患有和不患有骨质疏松症(基于骨密度，BMD)和/或继发骨折的女性。 本项目的目标是探索机器学习(ML)来改进骨质疏松风险评估 年轻的绝经后妇女。先前基于ML的骨质疏松症和相关骨折分析已经存在，但仍在进行中 非美国人口和/或人口规模有限。我们将利用大型妇女健康倡议(WHI)研究 (来自美国的160,000人)，开发、验证和比较不同的机器学习 方法(随机森林；Logistic回归；动态信念网络，DBN)用于年轻的绝经后 女人。ML模型将被构建并评估两个任务：1)预测老年女性的骨折风险 50-(目标1)；以及2)预测骨质疏松症(根据骨密度；目标2)。在每种情况下，我们都将使用 来自当前工具的现有风险因素，以及添加从WHI收集的其他变量，以确定新的 可提高预测能力的功能。我们还将通过构建DBN来评估时间模型的价值， 使用个人过去的观察来指导预测。我们将计算技术性能指标 (例如，敏感度、特异度、阳性预测值)，并进行误差分析以对比什么(子)组 每个型号(中)都能正确识别。我们还将进行敏感性分析，以确定不同 变量对模型预测的稳健性的影响。最后，我们计划对模型进行外部验证(目标3 从目标1和目标2使用来自加州大学洛杉矶分校和加州大学旧金山分校的电子健康记录(EHR)数据集，调查学位 可携带性。R21的成功实施将：1)开发和测试不同的ML模型预测主要 美国女性的骨质疏松性骨折和骨质疏松；2)确定潜在的额外变量，以告知风险 这些条件；以及3)洞察这些ML模型可以通过分层改进的领域。 阳离子和/或未来的方法学方法。R21的结果将作为更广泛的 R01开发更有效的骨折和骨质疏松风险预测模型。

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.