Clinical trials: Predictive Learning Models
Kidney Transplantation, Delirium - Postoperative
A Predictive Model for Postoperative Delirium in Kidney Transplant Patients
This study aims to develop and prospectively validate a machine learning-based prediction model for postoperative delirium in kidney transplant recipients, using perioperative clinical data.
- Conditions
- Kidney Transplantation, Delirium - Postoperative, Cognitive
- Trial name
- NCT07078578
- Volunteers
- 4,800 people
- Sponsor
- Hua Zheng
Difficult Airway Intubation
Research on a Machine Learning-Based Predictive Model for Difficult Intubation Using Specific Vocal Characteristics
1. Study Purpose This research aims to develop a novel, non-invasive, and simple method to predict difficult intubation before surgery.
- Trial name
- NCT07278232
- Testing
- No Intervention: Observational Cohort
- Where
- China
- Volunteers
- 300 people
- Sponsor
- Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Renal Transplanted Recipients, Kidney Function
Renal Function Prediction in Living Donor Transplant Using Baseline Data
This study aims to predict early post-transplant kidney function in living donor kidney transplant recipients using baseline characteristics of donors and recipients.
- Trial name
- NCT06728995
- Where
- South Korea
- Volunteers
- 3,500 people
- Sponsor
- Seoul National University Hospital
Sepsis
A Machine Learning Predictive Model for Sepsis
Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis.
- Trial name
- NCT04771429
- Where
- China
- Volunteers
- 4,500 people, healthy volunteers welcome
- Sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Gangrenous Cholecystitis
Construction of a Predictive Model of Gangrenous Cholecystitis Based on Machine Learning
Gangrenous cholecystitis is the most common complication of acute cholecystitis. There is no research using machine learning models to construct predictive diagnostic models for gangrenous cholecystitis.
- Trial name
- NCT06399081
- Testing
- Observational
- Where
- China
- Volunteers
- 1,006 people
- Sponsor
- Dalian Medical University
Erythrocyte Transfusion, Machine Learning
Machine Learning Versus Traditional Scores in Predicting Erythrocyte Need
In this study, we compared perioperative bleeding prediction scores with our machine learning-based prediction system in predicting the need for erythrocyte suspension during cardiovascular surgery.
- Trial name
- NCT06594484
- Testing
- Ml Based Algorithm 1, Ml Based Algroithm 2, Bleeding Scores
- Where
- Turkey (Türkiye)
- Volunteers
- 430 people
- Sponsor
- Kocaeli City Hospital
Sexually Transmitted Infections (STIs), HIV
Development of a Predictive Model for Sexually Transmitted Infections in Individuals Using Pre-Exposure Prophylaxis for HIV in Spain
Objective: To develop and validate a predictive model for acquiring sexually transmitted infections (STIs) in individuals using HIV pre-exposure prophylaxis (PrEP) within the national program providing this strategy.
- Conditions
- Sexually Transmitted Infections (STIs), HIV, PrEP
- Trial name
- STI_PrEPare (NCT06860945)
- Testing
- No Intervention: Observational Cohort
- Where
- 26 sites in Spain
- Volunteers
- 9,000 people, healthy volunteers welcome
- Sponsor
- Hospital Universitario de Valme
BIS, BIS-EEG
Deep Learning Framework for Continuous Depth of Anesthesia Forecasting
The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive.
- Conditions
- BIS, BIS-EEG, Artifical Intelligence, Intraoperative
- Trial name
- NCT07536230
- Where
- Belgium
- Volunteers
- 115 people, healthy volunteers welcome
- Sponsor
- Universitair Ziekenhuis Brussel
Posterior Fossa Tumors, Pediatric Tumor of Brain
Predicting Cognitive Dysfunction After Pediatric Posterior Fossa Tumor Surgery
The goal of this observational study is to develop and validate a clinical prediction model to identify risk factors for long-term cognitive dysfunction in children (ages 0-18 years) who have undergone surgical resection of a…
- Trial name
- NCT07350382
- Testing
- Not applicable- observational study
- Where
- China
- Volunteers
- 600 people
- Sponsor
- West China Hospital
Severe Malaria
Clinical Prediction Models for Pediatric In-Hospital Death Risk in Congolese Severe Malaria Children Using Machine Learning Based-Algorithms
The goal of this observational study is to optimize the management of severe childhood malaria, based on understanding and controlling the severity factors of the disease in Congolese children aged 2 to 9 years (the age group at…
- Trial name
- NCT07355426
- Testing
- Puncture of the radial artery for instant arterial blood gaz as well as for venous biochemistry
- Where
- Democratic Republic of the Congo
- Volunteers
- 100 people
- Sponsor
- University of Kinshasa