Komaki Shotaro, Muranaga Fuminori, Uto Yumiko, Iwaanakuchi Takashi, Kumamoto Ichiro
Graduate School of Medical and Dental Science, 12851Kagoshima University, Japan.
Kagoshima Medical Professional College, Japan.
Eval Health Prof. 2021 Dec;44(4):436-442. doi: 10.1177/01632787211014270. Epub 2021 May 3.
Nursing records are an account of patient condition and treatment during their hospital stay. In this study, we developed a system that can automatically analyze nursing records to predict the occurrence of diseases and incidents (e.g., falls). Text vectorization was performed for nursing records and compared with past case data on aspiration pneumonia, to develop an onset prediction system. Nursing records for a patient group that developed aspiration pneumonia during hospitalization and a non-onset control group were randomly assigned to definitive diagnostic (for learning), preliminary survey, and test datasets. Data from the preliminary survey were used to adjust parameters and influencing factors. The final verification used the test data and revealed the highest compatibility to predict the onset of aspiration pneumonia (sensitivity = 90.9%, specificity = 60.3%) with the parameter values of size = 80 (number of dimensions of the sentence vector), window = 13 (number of words before and after the learned word), and min_count = 2 (threshold of wordcount for word to be included). This method represents the foundation for a discovery/warning system using machine-based automated monitoring to predict the onset of diseases and prevent adverse incidents such as falls.
护理记录是患者住院期间病情和治疗情况的记录。在本研究中，我们开发了一个系统，该系统可以自动分析护理记录以预测疾病和事件（如跌倒）的发生。对护理记录进行文本向量化，并与既往吸入性肺炎病例数据进行比较，以开发发病预测系统。将住院期间发生吸入性肺炎的患者组和未发病对照组的护理记录随机分配到确诊诊断（用于学习）、初步调查和测试数据集。初步调查的数据用于调整参数和影响因素。最终验证使用测试数据，结果显示在大小 = 80（句子向量的维度数）、窗口 = 13（学习单词前后的单词数）和最小计数 = 2（单词包含的词数阈值）的参数值下，预测吸入性肺炎发病的兼容性最高（敏感性 = 90.9%，特异性 = 60.3%）。该方法是基于机器自动监测的疾病发病预测和跌倒等不良事件预防发现/预警系统的基础。
