Machine Learning-Based Phenotyping of IBS Patients Using Multidimensional Patient-Reported Outcomes
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 500
- 主要终点
- Distinct patient subgroups (phenotypes) identified through unsupervised machine learning clustering algorithms.
研究概览
简要总结
The purpose of this study is to identify distinct subgroups (phenotypes) of Irritable Bowel Syndrome (IBS) patients. While traditional IBS classification relies mainly on bowel habits, this research uses a multidimensional questionnaire to capture clinical symptoms, psychological factors, diet triggers, sleep quality, and digital behaviors. By applying advanced machine learning algorithms to these patient-reported outcomes, the study aims to uncover hidden patterns that can help customize future treatments and improve patient care.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Participants aged 18 years or older.
- •Formal physician-confirmed diagnosis of Irritable Bowel Syndrome (IBS).
- •Ability to access and complete the digital multidimensional questionnaire.
- •Willingness to provide digital informed consent.
排除标准
- •Presence of organic gastrointestinal diseases (e.g., Inflammatory Bowel Disease, celiac disease, or colorectal cancer).
- •Presence of alarm/red flag symptoms (e.g., nocturnal symptoms waking the patient).
- •Incomplete or inconsistent questionnaire responses.
结局指标
主要结局
Distinct patient subgroups (phenotypes) identified through unsupervised machine learning clustering algorithms.
时间窗: At the time of questionnaire completion (Baseline)
次要结局
未报告次要终点
研究者
Khadija Ahmed Mhrose Glal
Director
Tanta University