A longitudinal machine learning approach providing clinicians timely detection to prevent military suicide

纵向机器学习方法为临床医生提供及时检测以防止军人自杀

• 批准号: 10727520
• 负责人: Chung-Yi Chiu
• 金额: $ 4.94万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10727520
• 关键词: longitudinal; machine; learning; approach; providing

PROJECT SUMMARY The suicide rates among U.S. military service members and Veterans (MV) remain alarmingly high. The suicide rate for active military service members has increased from 20.4 suicides in 2014 to 28.7 suicides in 2020 per 100,000. Veterans’ suicide rates have remained high, approximately 2 times higher than the general population (14.5 per 100,000). Unfortunately, the current suicide approaches from the Department of Defense and the Department of Veterans Affairs are insufficient. Further, recent literature shows inconsistent findings of suicide causes and suicide attempts across measures and time points, and lack of effectiveness of suicide screening and interventions. This is problematic for proactively and effectively preventing and stopping suicide among the MV populations. Additionally, less research has focused on suicide ideation than suicide completion/deaths, which means we are ultimately missing the first chance to stifle suicide and address risk factors. We will use secondary datasets and innovative machine learning (ML) to develop early screening and intervention modeling to address military suicide issues. The study will apply data-driven ML to improve MV healthcare quality by accelerating the implementation of patient-centered outcomes research, using several personalized-contextual variables of 10 clinically applicable dimensions, to predict suicide risk levels. Further, we will develop person-centered, context-sensitive ML modeling for suicide ideation (SI) and suicide attempt (SA) data-visualization profiles, which will assist in clinical screening, evaluation, and intervention. Our specific aims are to (1) establish ML algorithms detecting SI/SA at different military statuses to inform clinicians and (2) develop an SI/SA cross-sectional and longitudinal risk data-visualization profile for clinicians. Our overarching goals are to demonstrate (1) a new SI/SA screening paradigm and (2) a new SI/SA prevention, evidence-based intervention, and policy-making model for the MV populations. We harness big data and innovative ML applications to provide a 360-degree view of MV patients, which will improve healthcare quality and MV patient outcomes, specifically decreasing SI/SA. Our project will exemplify the Healthcare Effectiveness and Outcomes Research mission to make healthcare safer, higher quality, more accessible, equitable, and affordable. Most importantly, we will ensure that clinical professionals and relevant stakeholders who serve the MV populations can understand and apply the study’s findings.

项目总结 美国军人和退伍军人(MV)的自杀率仍然高得惊人。自杀 现役军人的自杀率从2014年的20.4人增加到2020年的28.7人 10万。退伍军人的自杀率一直居高不下，大约是普通人群的两倍 (每10万人14.5人)。不幸的是，目前国防部和 退伍军人事务部是不够的。此外，最近的文献显示自杀的结果不一致。 不同措施和时间点的原因和自杀企图，以及自杀筛查缺乏有效性 和干预措施。这对于主动有效地预防和阻止老年人自杀是有问题的。 MV种群。此外，更少的研究关注自杀意念，而不是自杀完成/死亡， 这意味着我们最终错过了扼杀自杀和解决风险因素的第一次机会。 我们将使用辅助数据集和创新的机器学习(ML)来开发早期筛查和 解决军人自杀问题的干预模式。这项研究将应用数据驱动的ML来提高MV 通过加速实施以患者为中心的结果研究，使用几个 个人化--10个临床适用维度的背景变量，用于预测自杀风险水平。此外， 我们将为自杀意念(SI)和自杀企图(SA)开发以人为中心的、上下文敏感的ML建模 数据可视化配置文件，这将有助于临床筛选、评估和干预。我们的具体目标 (1)建立在不同军事状态下检测SI/SA的ML算法，以告知临床医生；(2)开发 供临床医生使用的SI/SA横断面和纵向风险数据可视化配置文件。我们的首要目标是 展示(1)新的SI/SA筛查范例和(2)新的基于证据的SI/SA预防 针对MV人群的干预和政策制定模式。 我们利用大数据和创新的ML应用程序提供MV患者的360度视图，这将 改善医疗质量和MV患者结局，特别是降低SI/SA。我们的项目将成为 医疗有效性和结果研究使命，使医疗更安全、更高质量、更 可及、公平和负担得起。最重要的是，我们将确保临床专业人员和相关人员 服务于MV人群的利益相关者可以理解和应用这项研究的发现。