Clinical trials: Machine Learning
Perioperative/Postoperative Complications
Perioperative Risk Calculator
The aim of this project is to develop a machine-learning model for calculating the risk of postoperative complications. In addition to the data collected during the premedication, the model will include all intraoperative values…
- Trial name
- PROTECT (NCT04092933)
- Volunteers
- 175,559 people
- Sponsor
- Technical University of Munich
Schizophrenia, Violence
Prediction of Violent Behavior in Patients With Schizophrenia by Multimodal Machine Learning
Patients with schizophrenia have a higher risk of committing violent crimes than the general population, and the relative risk of violence against others is four times higher than the general population.
- Trial name
- NCT04520399
- Testing
- none intervention
- Where
- China
- Volunteers
- 200 people
- Sponsor
- Shanghai Mental Health Center
Decompensation, Heart, Decompensation; Heart, Congestive
Inpatient Mortality Prediction Algorithm Clinical Trial (IMPACT)
Through the mapping of retrospective patient data into a discrete multidimensional space, a novel algorithm for homeostatic analysis, was built to make outcome predictions.
- Conditions
- Decompensation, Heart, Decompensation; Heart, Congestive, Death
- Trial name
- IMPACT (NCT03212534)
- Testing
- Patient mortality prediction
- Where
- United States
- Sponsor
- Dascena
Ovarian Cysts, Ovarian Cancer
Radiomics and Machine Learning in the Diagnosis of Ovarian Masses
The correct differential diagnosis between benign and malignant adnexal masses is the main goal of preoperative ultrasound diagnostics and is very important to plan the correct treatment for the patient in terms of surgical team…
- Trial name
- Multi-AROMA (NCT06342934)
- Testing
- Ultrasound
- Where
- Italy
- Volunteers
- 1,000 people
- Sponsor
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milano
Infection, Bloodstream
BACTERIUM: Study for a Machine-learning-based Model to Predict Bloodstream Infections
An increase of healthcare-associated infections caused by multidrug- resistant organisms (MRDO) is currently observed. One of the main causes of the emergence of a MDRO infection is an overuse of antibiotics.
- Trial name
- BACTERIUM (NCT05876598)
- Testing
- antibiotic treatments
- Where
- Italy
- Volunteers
- 5,000 people
- Sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Machine Learning, Acute Kidney Injury
Machine Learning Predict Renal Replacement Therapy After Cardiac Surgery
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality.
- Conditions
- Machine Learning, Acute Kidney Injury, Renal Replacement Therapy, Prediction Models
- Trial name
- NCT04977687
- Where
- China
- Volunteers
- 2,108 people
- Sponsor
- Chinese PLA General Hospital
Traumatic Brachial Plexus Lesion, Chronic Pain
Electroencephalographia as Predictor of Effectiveness HD-tDCS in Neuropathic Pain: Machine Learning Approach
Contextualization: Neuropathic pain is a complication present in the clinical picture of patients with traumatic Brachial Plexus injury (BPI).
- Trial name
- NCT04852536
- Testing
- Neurostimulation (High Definition - transcranial Direct Current Stimulation) HD-tDCS
- Where
- Brazil
- Volunteers
- 30 people
- Sponsor
- Federal University of Paraíba
Breast Cancer
Big Data and Text-mining Technologies Applied for Breast Cancer Medical Data Analysis
Primary purpose : To develop a method to automatically extract and structure the information included in numerous medical records from breast cancer patients.
- Trial name
- SENOMETRY (NCT02810093)
- Testing
- retrospective medical records analyze
- Where
- France
- Volunteers
- 10,000 people
- Sponsor
- University Hospital, Strasbourg, France
Intrahepatic Cholangiocarcinoma (Icc)
Liquid Biopsy Using Exosomal miRNA Enables Risk Stratification of Potential Metastasis in Patients With Intrahepatic Cholangiocarcinoma.
Occult metastasis at the time of surgery is a major driver of poor outcomes in intrahepatic cholangiocarcinoma (ICC), yet reliable preoperative biomarkers to identify such patients are lacking.
- Trial name
- EXOMIC (NCT07224737)
- Testing
- EXOMIC small RNA sequencing, EXOMIC assay (qRT-PCR validation)
- Where
- United States
- Volunteers
- 230 people
- Sponsor
- City of Hope Medical Center
PTSD - Post Traumatic Stress Disorder, Trauma-related Nightmares
Validation of an Algorithm for Predicting Awakenings Associated With Trauma-related Nightmares, Based on the Analysis of Physiological Patterns in Military and Former Military Patients Suffering From Post-traumatic Stress Disorder
The goal of this clinical trial is to validate an artificial intelligence algorithm called CAUD-PIN (CAUchemar Detection on INdividualized Parameters) to predict nightmare-related awakenings in military personnel and veterans…
- Trial name
- CAUD-PIN (NCT07851480)
- Testing
- CAUD-PIN (CAUchemar Detection on INdividualized Parameters) - AI-based predictive algorithm forTrauma-related nightmare awakenings
- Where
- 3 sites in France
- Volunteers
- 27 people
- Sponsor
- Direction Centrale du Service de Santé des Armées
Cardiotoxicity, DPP3
Cardiovascular Complications in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation.
Allogeneic hematopoietic stem cell transplantation (HSCT) represents a major therapeutic strategy for malignant hematologic diseases, with the number of procedures steadily increasing in France each year.
- Conditions
- Cardiotoxicity, DPP3, HSCT, Allogeneic Hematopoietic Stem Cell Transplantation
- Trial name
- ALLOCARDIOTOX (NCT07157670)
- Where
- France
- Volunteers
- 400 people
- Sponsor
- Assistance Publique - Hôpitaux de Paris
Ischemic Stroke, Hemorrhagic Stroke
Using Machine Learning and Biomarkers for Early Detection of Delayed Cerebral Ischemia
The overall goal of this project is to determine if machine learning and analysis of neurospecific biomarkers can enable early detection of upcoming or ongoing cerebral ischaemia in patients suffering from subarachnoid…
- Trial name
- CIDAIBASSAH (NCT06069973)
- Testing
- No intervention, observational study
- Where
- Sweden
- Volunteers
- 1,500 people
- Sponsor
- Sahlgrenska University Hospital
Locally Advanced Rectal Cancer (LARC)
AI-Based Prediction of Pathological Response in Rectal Cancer Patients Receiving Total Neoadjuvant Therapy
This study aims to better understand how body composition, inflammation, and nutrition affect how rectal cancer responds to treatment.
- Trial name
- CINR-AI (NCT07049627)
- Testing
- Total Neoadjuvant Therapy (TNT)
- Where
- 2 sites in Turkey (Türkiye)
- Volunteers
- 93 people
- Sponsor
- Ankara Etlik City Hospital
Critical Illness
Evaluation of Clinical Implementation of Machine Learning Based Decision Support for ICU Discharge
Unexpected intensive care unit (ICU) readmission is associated with longer length of stay and increased mortality. Bedside decision support may prevent readmission and mortality and may allow optimizing ICU capacity.
- Trial name
- MIRACLE (NCT05497505)
- Testing
- Pacmed Critical
- Where
- 2 sites in Netherlands
- Volunteers
- 1,500 people
- Sponsor
- Patrick J. Thoral
Refractive Errors, Cycloplegic Refraction
ML Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults
This study presents a machine learning model that predicts cycloplegic refraction in adults with myopia using standard non-cycloplegic eye measurements, aiming to reduce the need for cycloplegic drops while still identifying…
- Conditions
- Refractive Errors, Cycloplegic Refraction, Accommodation
- Trial name
- NCT07690813
- Testing
- Machine learning model for predicting cycloplegic refraction
- Where
- China
- Volunteers
- 2,500 people
- Sponsor
- Second Affiliated Hospital of Nanchang University
Spleen Injury, Machine Learning
Deep Learning Algorithm for Traumatic Splenic Injury Detection and Sequential Localization
Spleen laceration is a lethal abdominal trauma and usually be diagnosed by medical images such as computed tomography. Deep learning had been proved its usage in detect abnormalities in medical images.
- Trial name
- NCT05643612
- Testing
- Deep learning algorithm
- Where
- Taiwan
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Chang Gung Memorial Hospital
Non-Muscle Invasive Bladder Cancer
Machine Learning-Based Prediction of BCG Response in High-Risk Non-Muscle Invasive Bladder Cancer Patients
This prospective study aims at using Artificial intelligence to create a helpful unbiased machine learning-based model that predicts BCG unresponsiveness in high risk BCG-naïve NMIBC patients incorporating all potential…
- Trial name
- NCT05332353
- Testing
- Bacillus Calmette-Guerin
- Where
- Egypt
- Volunteers
- 100 people
- Sponsor
- Mansoura University
Atrial Fibrillation
A Study to Assess the Effectiveness of an Atrial Fibrillation (AF) Risk Prediction Algorithm and Diagnostic Test in Identifying Patients With AF.
This is a trial to assess the effectiveness of an atrial fibrillation (AF) risk prediction algorithm and diagnostic test for the identification of patients with atrial fibrillation
- Trial name
- PULsE AI (NCT04045639)
- Where
- 6 sites in United Kingdom
- Volunteers
- 260 people
- Sponsor
- Bristol-Myers Squibb
Heart Failure, Pulmonary Arterial Hypertension
Non-invasive Pulmonary Artery Prediction (ADOPTS)
A proprietary machine-learning algorithm has been developed to model continuous pulmonary artery pressure (PAP), a physiologic marker of cardiopulmonary function.
- Trial name
- ADOPTS (NCT05641675)
- Testing
- right heart catheterization
- Where
- United States
- Volunteers
- 25 people
- Sponsor
- Silverleaf Medical Sciences INC
Patient Safety
Machine Learning Prediction of Parameters of Early Warning Scores in Intensive Care Units
A large number of different organ functions are recorded in real time for patients being monitored in an intensive care unit. On the one hand, the measured values collected are used for continuous monitoring of vital parameters…
- Trial name
- AIM-PEW-ICU (NCT06259812)
- Testing
- Parameters of Early Warning Scores
- Where
- Austria
- Volunteers
- 8,000 people, healthy volunteers welcome
- Sponsor
- Kepler University Hospital