TY - JOUR AU - Cuenca-Zaldívar, Juan Nicolás AU - Torrente-Regidor, Maria AU - Martín-Losada, Laura AU - Fernández-De-Las-Peñas, César AU - Florencio, Lidiane Lima AU - Sousa, Pedro Alexandre AU - Palacios-Ceña, Domingo PY - 2022 DA - 2022/5/12 TI -利用电子护理健康记录探索COVID-19大流行第一波期间住院患者的情绪和护理管理:描述性研究JO - JMIR Med Inform SP - e38308 VL - 10 IS - 5kw -电子健康记录KW - COVID-19 KW -大流行KW -内容文本分析AB -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:本研究旨在通过检查《新冠肺炎疫情信息网络》的自由文本分析积极或消极情绪，比较《新冠肺炎疫情信息网络》与未入院患者的情绪，对新冠肺炎疫情第一波开始时患者的情绪进行时间分析，确定《新冠肺炎疫情信息网络》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后重症监护病房的住院患者，另一组来自无COVID-19的住院患者。使用加拿大国家研究委员会、Affin和必应词典对词法化文本进行情感分析。使用必应字典、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。应用机器学习技术来评估患有和未患COVID-19的患者组的ENCN是否存在显著差异。 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. SN - 2291-9694 UR - https://medinform.www.mybigtv.com/2022/5/e38308 UR - https://doi.org/10.2196/38308 UR - http://www.ncbi.nlm.nih.gov/pubmed/354869 DO - 10.2196/38308 ID - info:doi/10.2196/38308 ER -