Clinical trials: Machine Learning
Cancer
Effect of Behavioral Nudges on Serious Illness Conversation Documentation
The main purpose of this research study is to evaluate the effectiveness of "nudges" to clinicians, to patients, or to both in increasing Serious Illness Conversation (SIC) documentation; and to identify moderators of…
- Trial name
- SPP2 (NCT04867850)
- Testing
- Usual Care, Clinician Nudge, Patient Nudge
- Where
- United States
- Volunteers
- 4,450 people
- Sponsor
- Abramson Cancer Center at Penn Medicine
Influenza, Vaccination
Encouraging Flu Vaccination Among High-Risk Patients Identified by ML
The purpose of the current study is to test different interventions to determine the most effective way to promote flu vaccine uptake in a high-risk population identified by an "artificial intelligence" (AI) or machine learning…
- Conditions
- Influenza, Vaccination, Health Promotion, Health Behavior
- Trial name
- NCT04323137
- Testing
- Risk reduction, Medical records-based recommendation, Algorithm-based recommendation
- Where
- United States
- Volunteers
- 117,649 people
- Sponsor
- Geisinger Clinic
Post-operative Delirium, Decision Support Systems, Clinical
Mitigation of Postoperative Delirium in High-Risk Patients
Among patients with cognitive impairment (CI) that undergo surgery, the risk for developing postoperative delirium (POD) is high (50%) and associated with further morbidity and mortality.
- Conditions
- Post-operative Delirium, Decision Support Systems, Clinical, Cognitive Impairment
- Trial name
- NCT05777187
- Testing
- Clinical Decision Support
- Where
- United States
- Volunteers
- 7,412 people
- Sponsor
- Icahn School of Medicine at Mount Sinai
Adolescent Well-Being, Mental Health Wellness
Predict the Best Level of Care Placement for Each Child's Behavioral Health Needs - Effectiveness Study
The purpose of this study is to test the effectiveness of a new clinical decision support tool, Placement Success Predictor (PSP), in a naturalistic setting.
- Trial name
- NCT06834763
- Testing
- Clinical team access to Placement Success Predictor (PSP) results
- Where
- United States
- Volunteers
- 403 people
- Sponsor
- Outcome Referrals, Inc.
Epilepsy
Acute Recording of Stereotyped High-Frequency Oscillations
Overall, this study will investigate the functional utility of stereotyped HFOs by capturing them with a new implantable system (Brain Interchange - BIC of CorTec), which can sample neural data at higher rates >=1kHz and deliver…
- Trial name
- NCT05439655
- Testing
- Brain Interchange System
- Where
- 2 sites in United States
- Volunteers
- 17 people
- Sponsor
- Mayo Clinic
Clinical Deterioration, Hospital Medicine
Realtime Streaming Clinical Use Engine for Medical Escalation
The escalation of care for patients in a hospitalized setting between nurse practitioner managed services, teaching services, step-down units, and intensive care units is critical for appropriate care for any patient.
- Conditions
- Clinical Deterioration, Hospital Medicine, Monitoring, Physiologic
- Trial name
- ReSCUE-ME (NCT04026555)
- Testing
- MEWS++ Monitoring, Predictor Score
- Where
- United States
- Volunteers
- 2,780 people
- Sponsor
- Icahn School of Medicine at Mount Sinai
Difficult Endotracheal Intubation, Artificial Intelligence
Anthropometric and US-Guided Difficult Intubation Prediction With ML Models
The assessment and management of difficult airway is of critical importance. Unsuccessful airway management leads to serious mortality and morbidity.
- Trial name
- NCT06904586
- Testing
- Thyromental distance, Neck circumference, Mouth opening distance
- Where
- Turkey (Türkiye)
- Volunteers
- 329 people
- Sponsor
- Duzce University
Accidental Falls, Risk Assessment
AI-Powered Fall Risk Prediction in Nursing Care
The goal of this study is to develop a nursing clinical decision support system for fall risk prediction using machine learning and computer vision techniques.
- Conditions
- Accidental Falls, Risk Assessment, Nursing Assessment, Machine Learning
- Trial name
- NCT07000981
- Where
- Turkey (Türkiye)
- Volunteers
- 177 people
- Sponsor
- Inonu University
Coronary Artery Disease, Ischemic Heart Disease
Volatilome and Single-Lead Electrocardiogram Optimize Ischemic Heart Disease Diagnosis Using Machine Learning Models
This is a prospective, case-control, single-center, observational, non-randomized study. It is designed to evaluate the diagnostic accuracy of functional tests involving physical exertion monitored via a 12-lead ECG, combined…
- Conditions
- Coronary Artery Disease, Ischemic Heart Disease, Chronic Coronary Disease, Angina Pectoris
- Trial name
- NCT06181799
- Testing
- Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).
- Where
- Russia
- Volunteers
- 80 people, healthy volunteers welcome
- Sponsor
- I.M. Sechenov First Moscow State Medical University
Colorectal Cancer
Improving Colorectal Cancer Screening in Racially Diverse Zip Codes Using Navigation and Machine Learning (PCSNaP)
The overarching goal of the "PCSNaP" Research Study is to support the Abramson Cancer Center (ACC) of the University of Pennsylvania in carrying out its mission to increase colorectal cancer (CRC) screening completion among…
- Trial name
- PCSNaP (NCT05383976)
- Testing
- Machine Learning Algorithm with Existing Penn Medicine CRC Patient Navigation Program
- Where
- United States
- Volunteers
- 385 people, healthy volunteers welcome
- Sponsor
- Abramson Cancer Center at Penn Medicine
Bone Age
Validation of an Artificial Intelligence-based Algorithm for Skeletal Age Assessment
The purpose of this study is to understand the effects of using an Artificial Intelligence algorithm for skeletal age estimation as a computer-aided diagnosis (CADx) system.
- Trial name
- NCT03530098
- Testing
- BoneAgeModel
- Where
- 6 sites in United States
- Volunteers
- 1,903 people
- Sponsor
- Stanford University
Gastrointestinal Hemorrhage
Artificial Intelligent Clinical Decision Support System Simulation Center Study for Technology Acceptance
The purpose of this research study is to measure the effect on of a large language model interface on the usability, attitudes, and provider trust when using a machine learning algorithm-based clinical decision support system in…
- Trial name
- NCT05816473
- Testing
- LLM
- Where
- United States
- Volunteers
- 108 people, healthy volunteers welcome
- Sponsor
- Yale University