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

预测谁会骨折：观察性妇女健康倡议研究数据集中机器学习的探索。

基本信息

批准号：
10707881
负责人：
ALEX BUI
金额：
$ 14.1万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-21 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10707881
关键词：
Acceleration Age Area Assessment tool Bayesian Network Belief Bone Density Bone Tissue Calibration Cessation of life Characteristics Classification Clinical Data Data Set Deterioration Discrimination Disease Early Intervention Electronic Health Record Femur Fracture Future Goals Guidelines Individual Inferior Life Logistic Regressions Longitudinal cohort Machine Learning Menopause Methodology Methods Modeling Neck Osteoporosis Osteoporotic Outcome Performance Population Postmenopause Predictive Value Prospective Studies Public Health ROC Curve Recommendation Risk Risk Assessment Risk Estimate Risk Factors Self Assessment Site Specificity Stratification Techniques Testing United States United States Preventative Services Task Force Validation Woman Women&apos s Health age group aged bone bone fragility bone loss bone mass candidate selection chronic pain design disability electronic health record system experience follow-up fracture risk gradient boosting human old age (65+)improved indexing insight machine learning model machine learning prediction model building osteoporosis with pathological fracture performance tests pharmacologic predictive modeling predictive tools random forest repository risk prediction screening screening guidelines tool

项目摘要

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.

项目摘要半数绝经后女性在其余生中将经历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危险之中非常重要进行早期干预和缓解，而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50-64岁的年轻绝经后女性进行选择性筛查基于风险评估工具（例如 OST、FRAX、SCORE）的使用但是，我们已经证明了这些。工具——并不是专门为这个年龄段的女性开发的——不能很好地区分患有或未患有骨质疏松症（基于骨矿物质密度，BMD）和/或随后骨折的女性。该项目的目标是探索机器学习（ML）以改善骨质疏松症风险评估先前存在基于 ML 的骨质疏松症和相关骨折分析，但仍在进行中。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），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）将针对两项任务构建和评估 ML 模型：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）。现有工具中的现有风险因素，以及添加从 WHI 收集的其他变量来识别新的风险因素我们还将通过构建 DBN 来评估时间模型的价值。我们将使用个人过去的观察来计算技术绩效指标。（例如，敏感性、特异性、阳性预测值）并进行误差分析以对比哪些（子）组每个模型都正确识别。我们还将进行敏感性分析，以确定不同模型的影响。最后，我们计划对模型进行外部验证（目标 3）。目标 1 和 2 使用 UCLA 和 UCSF 的电子健康记录 (EHR) 数据集来调查学位成功执行此 R21 将：1）开发和测试预测主要内容的不同 ML 模型。美国女性骨质疏松性骨折和骨质疏松症；2) 确定潜在的额外变量以告知风险这些条件；3）深入了解可以通过分层改进此类 ML 模型的领域 R21 的结果将作为更广泛的基准。 R01 开发更有效的骨折和骨质疏松风险预测模型。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Race and Ethnicity and Fracture Prediction Among Younger Postmenopausal Women in the Women's Health Initiative Study.

妇女健康倡议研究中年轻绝经后妇女的种族和民族以及骨折预测。

DOI：
发表时间：
2023-07-01
期刊：
影响因子：
39
作者：
Crandall, Carolyn J;Larson, Joseph C;Schousboe, John T;Manson, JoAnn E;Watts, Nelson B;Robbins, John A;Schnatz, Peter;Nassir, Rami;Shadyab, Aladdin H;Johnson, Karen C;Cauley, Jane A;Ensrud, Kristine E
通讯作者：
Ensrud, Kristine E