Nakatani Hayao, Nakao Masatoshi, Uchiyama Hidefumi, Toyoshiba Hiroyoshi, Ochiai Chikayuki
NTT Medical Center Tokyo, Tokyo, Japan.
Pharmaceutical Research Department, Global Pharmaceutical R&D Division, Neopharma Japan Co Ltd, Tokyo, Japan.
JMIR Med Inform. 2020 Apr 22;8(4):e16970. doi: 10.2196/16970.
Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly.
The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input-unstructured nursing records obtained from Japanese electronic medical records (EMRs)-using a natural language processing (NLP) algorithm and machine learning.
The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis.
The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records.
We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.
医院内跌倒为影响住院患者安全的最常见风险因素，可导致严重伤害。因此，预防跌倒为医疗保健机构风险管理的最重要领域之一。然而，现有的跌倒预测方法既费力又昂贵。
本研究旨在验证能否通过使用自然语言处理（NLP）算法和机器学习分析从日本电子病历（EMR）中获取的单一输入非结构化护理记录来预测医院住院患者跌倒。
从一家急症医院的EMR中提取335例跌倒患者和408例未跌倒患者12个月期间的护理记录，并随机分为学习数据集和测试数据集。对前一个数据集进行NLP和机器学习，以提取有助于区分跌倒患者和未跌倒患者的词素，从而构建跌倒预测模型。然后，使用后一个数据集通过受试者工作特征（ROC）分析来确定模型的预测价值。
使用测试数据集进行跌倒预测显示出较高的准确性，在五项独立实验中，ROC曲线下面积、敏感性、特异性和比值比的平均值分别为0.834（标准差0.005）、0.769（标准差0.013）、0.785（标准差0.020）和12.27（标准差1.11）。纳入最终模型的词素包括许多与已知跌倒风险因素密切相关的词汇，如使用精神药物、意识状态和活动能力，从而表明NLP算法与机器学习相结合可有效从护理记录中提取跌倒风险因素。
我们成功证实，通过使用NLP算法和机器学习分析护理记录可预测医院住院患者跌倒。因此，有可能开发一种跌倒风险监测系统，该系统每日分析护理记录，并在住院患者跌倒风险增加时向医护人员发出警报。
