Using Natural Language Processing of Novel Biopsychosocial Disease Constructs to Aid in the Evaluation and Diagnosis of Functional Gastrointestinal Disorders
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 入组人数
- 700
- 试验地点
- 2
- 主要终点
- Latent themes present in patient descriptions of FGIDs symptoms
研究概览
简要总结
The study has two arms, where the same natural language processing (NLP) and probabilistic graphical modeling technology will be utilized on patients' report of symptoms in both arms. The clinical arm is focused on patients presenting for consultation with a gastroenterologist. The endoscopy arm is focused generally on patients presenting for a diagnostic endoscopy, with the goal of capturing Functional Gastrointestinal Disorder (FGID) patients prior to diagnosis.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Scheduled for consult in the Motility Clinic in the MGH Gastroenterology Unit or for diagnostic endoscopy in the MGH Gastroenterology Unit
- •Patient must agree to have their interactions audio-recorded
- •Informed consent form signed by the subjects
排除标准
- •Non-native English speaker
- •Patients unable to communicate their own symptoms
研究组 & 干预措施
Patients scheduled for consult with gastroenterologist
Patients scheduled for diagnostic endoscopy
结局指标
主要结局
Latent themes present in patient descriptions of FGIDs symptoms
时间窗: 08/09/2018-08/09/2023
Latent themes present in patient descriptions of FGIDs symptoms as generated by machine learning as well as quantitative comparisons to traditional metrics of patient descriptions including Rome IV criteria and patient descriptions of severity.
次要结局
未报告次要终点
研究者
Kyle Staller, MD, MPH
Principal Investigator
Massachusetts General Hospital