Prediction of Expected Length of Hospital Stay Using Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 撤回
- 入组人数
- 500
- 试验地点
- 2
- 主要终点
- Length of Stay
研究概览
简要总结
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict length of stay throughout a patient's admission. This algorithm was then validated in a validation cohort.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
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
- •Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
排除标准
- 未提供
研究组 & 干预措施
Validation
A subset of patients that are "held back" and used to validate the algorithm's accuracy.
Training
A subset of patients that are used to train the machine learning algorithm.
结局指标
主要结局
Length of Stay
时间窗: From date of admission to date of discharge (1 to 24 days)
The time spent by each patient in Home Hospital from time of admission to time of discharge, measured in hours
次要结局
未报告次要终点
研究者
David Levine
Attending Physician
Brigham and Women's Hospital