Asymmetric Operations Sector, Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA.
Research & Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA.
Mil Med. 2024 Aug 19;189(Suppl 3):399-406. doi: 10.1093/milmed/usae141.
Deployment-limiting medical conditions (DLMCs) such as debilitating injuries and conditions may interfere with the ability of military service members (SMs) to deploy. SMs in the United States (U.S.) Department of the Navy (DoN) with DLMCs who are not deployable should be placed in the medically restricted status of limited duty (LIMDU) or referred to the Physical Evaluation Board (PEB) for Service retention determination. It is critical to identify SMs correctly and promptly with DLMCs and predict their return-to-duty (RTD) to ensure the combat readiness of the U.S. Military. In this study, an algorithmic approach was developed to identify DoN SMs with previously unidentified DLMCs and predict whether SMs on LIMDU will be able to RTD.
Five years of historical data (2016-2022) were obtained from inpatient and outpatient datasets across direct and purchased care from the Military Health System (MHS) Data Repository (MDR). Key fields included International Classification of Diseases diagnosis and procedure codes, Current Procedure Terminology codes, prescription medications, and demographics information such as age, rank, gender, and service. The data consisted of 44,580,668 medical encounters across 1,065,224 SMs. To identify SMs with unidentified DLMCs, we developed an ensemble model combining outputs from multiple machine learning (ML) algorithms. When the ML ensemble model predicted a SM to have high risk scores, despite appearing healthy on administrative reports, their case was reviewed by expert clinicians to investigate for previously unidentified DLMCs; and such feedback served to validate the developed algorithms. In addition, leveraging 1,735,422 encounters (60,433 SMs) from LIMDU periods, we developed four separate ML models to estimate RTD probabilities for SMs after each medical encounter and predict the final LIMDU outcome.
The ensemble model had 0.91 area under the receiver operating characteristic curve (AUROC). Out of 236 (round one) and 314 (round two) SMs reviewed by clinicians, 127 (54%) and 208 (66%) SMs were identified with a previously unidentified or undocumented DLMC, respectively. Regarding predicting RTD for SMs placed on LIMDU, the best performing ML model achieved 0.76 AUROC, 68% sensitivity, and 71% specificity.
Our research highlighted potential benefits of using predictive analytics in a medical assessment to identify SMs with DLMCs and to predict RTD outcomes once placed on LIMDU. This capability is being deployed for real-time clinical decision support to enhance health care provider's deployability assessment capability, improve accuracy of the DLMC population, and enhance combat readiness of the U.S Military.
限制部署的医疗条件（DLMCs），如严重损伤和疾病，可能会影响军人的部署能力。美国海军（DoN）中无法部署的患有 DLMC 的军人应被置于有限责任的限制役（LIMDU）医疗限制状态，或提交给体检委员会（PEB）以确定是否留用。及时准确地识别患有 DLMC 的军人并预测他们的归队（RTD）时间对于确保美国军队的战备能力至关重要。在这项研究中，我们开发了一种算法方法来识别以前未被识别的 DoN 军人的 DLMC，并预测处于 LIMDU 的军人是否能够归队。
从军事医疗系统（MHS）数据存储库（MDR）的直接和购买护理的住院和门诊数据集中获得了五年的历史数据（2016-2022 年）。关键字段包括国际疾病分类诊断和程序代码、当前程序术语代码、处方药物以及年龄、军衔、性别和服务等人口统计信息。该数据包括 44580668 次医疗就诊，涉及 1065224 名军人。为了识别患有未被识别的 DLMC 的军人，我们开发了一个结合多个机器学习（ML）算法输出的集成模型。当 ML 集成模型预测一个军人有高风险评分时，尽管他们在行政报告中看起来健康，但他们的病例会由专家临床医生进行审查，以调查以前未被识别的 DLMC；这种反馈有助于验证所开发的算法。此外，利用 LIMDU 期间的 1735422 次就诊（60433 名军人），我们开发了四个独立的 ML 模型，以估计每次医疗就诊后军人的 RTD 概率，并预测最终的 LIMDU 结果。
该集成模型的接收器操作特征曲线下面积（AUROC）为 0.91。在由临床医生审查的 236 名（第一轮）和 314 名（第二轮）军人中，分别有 127 名（54%）和 208 名（66%）军人被确定患有以前未被识别或未记录的 DLMC。关于预测处于 LIMDU 的军人的 RTD，表现最佳的 ML 模型达到了 0.76 的 AUROC、68%的敏感性和 71%的特异性。
我们的研究强调了在医疗评估中使用预测分析来识别患有 DLMC 的军人并预测他们处于 LIMDU 后的 RTD 结果的潜在好处。这项能力正在实时临床决策支持中部署，以增强医疗保健提供者的部署能力评估能力、提高 DLMC 人群的准确性，并增强美国军队的战备能力。
