CTRI/2024/06/068341尚未招募Unknown
Developing machine learning predictive models for personalized PEFR risk assessment in upper abdominal surgery patients: An observational study - NI
Dr Lairenjam Deepa Devi0 个研究点目标入组 0 人开始时间: 待定最近更新:
试验速览
- 阶段
- Unknown
- 状态
- 尚未招募
- 发起方
研究概览
简要总结
暂无简介。
研究设计
- 研究类型
- Observational
入排标准
入选标准
- •1. Patients scheduled for elective upper abdominal surgeries e.g. cholecystectomy, gastrectomy, hernia repair either open or laparoscopic.
- •2. Adults aged 18years and above.
排除标准
- •1. Emergency surgeries.
- •2. Patients unable to provide informed consent.
- •3. Patients who are unable to comprehend and perform PEFR
研究者
相似试验
招募中
Unknown
Development of Machine Learning Models to Predict Delirium After On-Pump Cardiac Surgery Based on Preoperative and Intraoperative IndicesCardiovascular surgeryJPRN-UMIN000049390Osaka University300
已完成
Unknown
Development and validation of prediction models for treatment failure of HFNC in COVID-19: a multicenter cohort studyCOVID-19JPRN-UMIN000050024Tohoku University300
招募中
Unknown
Artificial Intelligence Predicts Refractory Septic Shock in Sepsis Patients: A Validated Model for Early predictioCTRI/2024/06/069604Mukkelli Vinay Gandhi
尚未招募
Unknown
Development of computer-based models (using machine learning) for improving identification and for better prediction of outcomes in patients with coronary artery disease (disease of the blood vessels of the heart).CTRI/2023/05/052610Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal
尚未招募
不适用
Development and Accuracy of a Prediction Model for Acute Kidney Injury in Postoperative Cardiovascular Surgery PatientsPostoperative cardiovascular surgery patientsJPRN-UMIN000046526Yokohama City University200
