Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study.
Jiayuan ZhangJing Fu QiuCheng LeiYang PuYubo ZhangJingyu ZhangHongfan YuXueyao SuYanyan HuangRuoyan GongLijun ZhangQiuling ShiPublished in: JMIR formative research (2023)
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.
Keyphrases
- electronic health record
- social media
- health information
- end stage renal disease
- newly diagnosed
- oxidative stress
- minimally invasive
- clinical decision support
- healthcare
- patient reported
- chronic kidney disease
- pregnant women
- peritoneal dialysis
- big data
- coronary artery disease
- combination therapy
- case report
- adverse drug
- acute coronary syndrome
- atrial fibrillation
- percutaneous coronary intervention
- deep learning
- radiofrequency ablation
- patient reported outcomes