Zhang Jiayuan, Xu Wei, Lei Cheng, Pu Yang, Zhang Yubo, Zhang Jingyu, Yu Hongfan, Su Xueyao, Huang Yanyan, Gong Ruoyan, Zhang Lijun, Shi Qiuling
State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China.
School of Public Health, Chongqing Medical University, Chongqing, China.
JMIR Form Res. 2023 Sep 1;7:e43995. doi: 10.2196/43995.
Unlike research project-based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis.
This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery.
Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined.
A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; P=.009), age of menophania (OR -1.02 , 95% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms.
Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients' health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.
与基于研究项目的健康数据收集（问卷调查和访谈）不同，社交媒体平台允许患者自由讨论其健康状况并获得同伴支持。以往文献指出，公共和私人社交平台均可作为分析的数据来源。
本研究旨在使用自然语言处理（NLP）技术，识别聚焦超声消融术后子宫肌瘤患者对术后生活质量和症状负担的担忧。
使用图像文本识别技术将医患微信群聊的截图转换为自由文本，并将其用作本研究的研究对象。在2010年至2020年间重庆海扶医院确诊的408例子宫肌瘤患者中，我们在超过900,000字的微信群聊记录中搜索症状负担信息。我们首先通过对30%的微信文本进行人工编码构建症状语料库，然后使用Python中的正则表达式基于该语料库从其余文本中抓取症状信息。我们将结果与对相同记录的人工审核（金标准）进行比较。最后，我们分析了人群基线数据与概念性症状之间的关系；对定量和定性结果进行了检验。
本研究共纳入408例子宫肌瘤患者；数据清理后获得190,000字的自由文本。患者的平均年龄为39.94（标准差6.81）岁，平均BMI为22.18（标准差2.78）kg/m²。7种主要症状的中位报告时间分别为21、26、57、2、18、30和49天。逻辑回归模型确定术前月经持续时间（比值比[OR]1.14，95%置信区间5.86 - 6.37；P = 0.009）、绝经年龄（OR -1.02，95%置信区间11.96 - 13.47；P = 0.03）以及肌瘤数量（OR 2.34，95%置信区间1.45 - 1.83；P = 0.04）和大小（OR 0.12，95%置信区间2.43 - 3.51；P = 0.04）是术后症状的显著危险因素。
通过NLP技术从社交媒体平台提取的非结构化自由文本可用于分析。通过提取有关患者健康相关生活质量的概念性信息，我们可以在患者恢复的不同阶段采取个性化治疗，以提高其生活质量。与人工审核相比，基于Python的自由文本数据挖掘可以准确提取症状负担并节省大量时间，可以最大限度地利用基于人群的电子健康记录中的现有信息进行比较效果研究。
