%0期刊文章%@ 2291- 9694% I JMIR出版物%V 10卡塔尔世界杯8强波胆分析% N 5% P 38308% T利用电子护理健康记录探索COVID-19大流行第一波住院患者的情绪和护理管理:描述性研究%A Cuenca-Zaldívar,Juan Nicolás %A Torrente-Regidor,Maria %A Martín-Losada,Laura %A Fernández-De-Las-Peñas,César %A Florencio,Lidiane Lima %A Sousa,Pedro Alexandre %A Palacios-Ceña,Domingo %+护理和卫生保健研究小组，Puerta de耶罗卫生研究所- Segovia de Arana, C Joaquín罗德里戈，1,Majadahonda, 28222，西班牙，34 639962935，jcuenzal@yahoo.es %K电子健康记录%K COVID-19 %K大流行%K内容文本分析%D 2022 %7 12.5.2022 %9原始论文%J JMIR Med Inform %G英文%X背景:COVID-19大流行改变了许多住院单位(或病房)的正常工作。很少有研究使用电子护理临床记录(ENCN)及其非结构化文本来识别患者感觉的变化和感兴趣的治疗程序。目的:本研究旨在通过对ENCN自由文本的检查，分析积极或消极情绪，比较有或没有COVID-19住院患者的ENCN情绪，对COVID-19第一波大流行开始时患者的情绪进行时间分析，并确定ENCN中的主题。方法:对ENCN的文本内容进行描述性分析。包括从CGM Selene电子健康记录系统中提取的2020年1月至6月在瓜达拉玛医院(西班牙马德里)的所有encn。分析了两组encn:一组来自COVID-19重症监护病房后住院患者，第二组来自没有COVID-19的住院患者。使用加拿大国家研究委员会、Affin和Bing词典对词源化文本进行了情感分析。使用Bing字典、SO字典V1.11和Spa字典作为放大器和减量器对句子进行极性分析。 Machine learning techniques were applied to evaluate the presence of significant differences in the ENCN in groups of patients with and those without COVID-19. Finally, a structural analysis of thematic models was performed to study the abstract topics that occur in the ENCN, using Latent Dirichlet Allocation topic modeling. Results: A total of 37,564 electronic health records were analyzed. Sentiment analysis in ENCN showed that patients with subacute COVID-19 have a higher proportion of positive sentiments than those without COVID-19. Also, there are significant differences in polarity between both groups (Z=5.532, P<.001) with a polarity of 0.108 (SD 0.299) in patients with COVID-19 versus that of 0.09 (SD 0.301) in those without COVID-19. Machine learning modeling reported that despite all models presenting high values, it is the neural network that presents the best indicators (>0.8) and with significant P values between both groups. Through Structural Topic Modeling analysis, the final model containing 10 topics was selected. High correlations were noted among topics 2, 5, and 8 (pressure ulcer and pharmacotherapy treatment), topics 1, 4, 7, and 9 (incidences related to fever and well-being state, and baseline oxygen saturation) and topics 3 and 10 (blood glucose level and pain). Conclusions: The ENCN may help in the development and implementation of more effective programs, which allows patients with COVID-19 to adopt to their prepandemic lifestyle faster. Topic modeling could help identify specific clinical problems in patients and better target the care they receive. %M 354869 %R 10.2196/38308 %U https://medinform.www.mybigtv.com/2022/5/e38308 %U https://doi.org/10.2196/38308 %U http://www.ncbi.nlm.nih.gov/pubmed/354869