Department of Management Science and Information Systems, Oklahoma State University, Stillwater, OK, USA.
Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
Health Informatics J. 2024 Oct-Dec;30(4):14604582241300025. doi: 10.1177/14604582241300025.
Patients with sarcopenia often go undetected in busy clinical practices since the muscle measurements are not easily incorporated into routine clinical practice. The current research fills the gap by utilizing unstructured clinical notes combined with structured data from electronic health records (EHR), to increase sarcopenia detection. We developed and evaluated four approaches to first extract clinical note features, then integrate with structured data for sarcopenia detection models. Case studies were used to demonstrate the interpretation of the results and show the important association between predictors and outcomes. Out of 1304 participants, 1055 were controls, 249 met at least one criterion for Sarcopenia. The best performing model which incorporated both data-driven and knowledge-driven approaches to integrate clinical note features demonstrated a higher mean area under the curve (AUC = 73.93%, (95% CI, 73.83-74.02)) compared to the baseline model (AUC 71.59%, (95%CI, 71.56-71.61)). The case study shows that the important clinical note predictors may contribute to detection of sarcopenia such as "cane", "walker", "unsteady", etc. Incorporating clinical note features in sarcopenia detection models can identify a greater number of patients at risk for sarcopenia, potentially leading to targeted muscle testing assessments and corresponding treatments to address sarcopenia.
患有肌肉减少症的患者在繁忙的临床实践中常常未被发现，因为肌肉测量不易纳入常规临床实践。本研究通过利用非结构化临床记录和电子健康记录（EHR）中的结构化数据，来增加肌肉减少症的检测，填补了这一空白。我们开发并评估了四种方法，首先提取临床记录特征，然后将其与结构化数据集成到肌肉减少症检测模型中。案例研究用于解释结果并显示预测因素与结果之间的重要关联。在 1304 名参与者中，有 1055 名是对照者，249 名至少符合肌肉减少症的一个标准。与基线模型（AUC = 71.59%，（95%CI，71.56-71.61））相比，表现最佳的模型（AUC = 73.93%，（95%CI，73.83-74.02）），该模型结合了数据驱动和知识驱动的方法来整合临床记录特征。案例研究表明，重要的临床记录预测因素可能有助于肌肉减少症的检测，例如“拐杖”、“助行器”、“不稳定”等。在肌肉减少症检测模型中纳入临床记录特征可以识别更多处于肌肉减少症风险中的患者，从而可能进行有针对性的肌肉测试评估和相应的治疗来解决肌肉减少症问题。
