PDF(Pubmed)
Abstract：
BACKGROUND: Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.
OBJECTIVE: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.
METHODS: Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model\'s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score.
RESULTS: Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms.
CONCLUSIONS: The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.
OBJECTIVE: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.
METHODS: Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model\'s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score.
RESULTS: Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms.
CONCLUSIONS: The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.
摘要：
背景：在依靠行政卫生数据时，对医院获得性压力性伤害（HAPI）的监视通常是次优的，众所周知，国际疾病分类(ICD)代码具有很长的延迟，并且编码不足。我们在自由文本笔记上利用自然语言处理（NLP）应用程序，特别是住院护理笔记，来自电子病历(EMR)，更准确、更及时地识别HAPI。
目的：这项研究旨在表明，基于EMR的表型算法比单独的ICD-10-CA算法更适合检测HAPI，而临床日志使用护理笔记通过NLP以更高的准确性记录。
方法：在2015年至2018年在卡尔加里进行的一项临床试验中，从当地三级急性护理医院的从头到脚皮肤评估中确定了患有HAPI的患者。艾伯塔省,加拿大。与出院摘要数据库链接后，从EMR数据库中提取试验期间记录的临床记录。在模型开发过程中，通过顺序正向选择处理了几种临床注释的不同组合。使用随机森林(RF)开发了用于HAPI检测的文本分类算法，极端梯度提升(XGBoost)，和深度学习模型。调整分类阈值以使该模型能够实现与基于ICD的表型研究相似的特异性。评估了每个模型的性能，并在指标之间进行了比较，包括灵敏度,正预测值，负预测值，和F1得分。
结果：本研究使用了来自280名符合条件的患者的数据，其中97例患者在试验期间出现HAPI.RF是最佳执行模型，灵敏度为0.464(95%CI0.365-0.563)，特异性0.984（95%CI0.965-1.000），F1评分为0.612（95%CI为0.473-0.751）。与先前报道的基于ICD的算法的性能相比，机器学习（ML）模型在不牺牲太多特异性的情况下达到了更高的灵敏度。
结论：基于EMR的NLP表型算法在HAPI病例检测中的性能优于单独的ICD-10-CA代码。EMR中每日生成的护理笔记是ML模型准确检测不良事件的宝贵数据资源。该研究有助于提高自动化医疗质量和安全监控。
目的：这项研究旨在表明，基于EMR的表型算法比单独的ICD-10-CA算法更适合检测HAPI，而临床日志使用护理笔记通过NLP以更高的准确性记录。
方法：在2015年至2018年在卡尔加里进行的一项临床试验中，从当地三级急性护理医院的从头到脚皮肤评估中确定了患有HAPI的患者。艾伯塔省,加拿大。与出院摘要数据库链接后，从EMR数据库中提取试验期间记录的临床记录。在模型开发过程中，通过顺序正向选择处理了几种临床注释的不同组合。使用随机森林(RF)开发了用于HAPI检测的文本分类算法，极端梯度提升(XGBoost)，和深度学习模型。调整分类阈值以使该模型能够实现与基于ICD的表型研究相似的特异性。评估了每个模型的性能，并在指标之间进行了比较，包括灵敏度,正预测值，负预测值，和F1得分。
结果：本研究使用了来自280名符合条件的患者的数据，其中97例患者在试验期间出现HAPI.RF是最佳执行模型，灵敏度为0.464(95%CI0.365-0.563)，特异性0.984（95%CI0.965-1.000），F1评分为0.612（95%CI为0.473-0.751）。与先前报道的基于ICD的算法的性能相比，机器学习（ML）模型在不牺牲太多特异性的情况下达到了更高的灵敏度。
结论：基于EMR的NLP表型算法在HAPI病例检测中的性能优于单独的ICD-10-CA代码。EMR中每日生成的护理笔记是ML模型准确检测不良事件的宝贵数据资源。该研究有助于提高自动化医疗质量和安全监控。
