[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"predictive-model\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:predictive-model":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,3,0,[8,42,71],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":30,"lastUpdatePostDateStruct":31,"startDateStruct":34,"completionDateStruct":36,"leadSponsor":38,"locationsCount":41},"100618587","development-and-pre-validation-of-a-machine-learning-based-prediction-algorithm-for-early-functional-recovery-in-patients-undergoing-hip-and-knee-replacement-surgery-100618587",false,"NCT07333560","Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery","Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.","FISIO_IA","Inclusion Criteria:\n\n* Adults aged 18 years or older\n* Patients underwent elective hip or knee arthroplasty.\n* Patients for whom postoperative physiotherapy was initiated.\n\nExclusion Criteria:\n\n* Patients who underwent surgery for oncologic disease, femoral fracture, or revision joint arthroplasty.\n* Patients for whom postoperative physiotherapy was not provided due to postoperative complications\n* clinical data are unavailable.","ALL","18 Years",{"count":20,"type":21},943,"ESTIMATED","OBSERVATIONAL","The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is:\n\nCan a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery?\n\nPatients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.",[25,26,27,28],"Artificial Intelligence (AI)","Machine Learning","Joint Replacement","Predictive Model","RECRUITING","2026-05-27",{"date":32,"type":33},"2026-06-01","ACTUAL",{"date":35,"type":33},"2026-03-09",{"date":37,"type":21},"2027-12",{"name":39,"class":40},"Istituto Ortopedico Rizzoli","OTHER",2,{"id":43,"slug":44,"hasResults":11,"nctId":45,"briefTitle":46,"officialTitle":47,"acronym":4,"eligibilityCriteria":48,"healthyVolunteers":49,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":50,"targetDuration":52,"studyType":22,"phases":4,"briefSummary":53,"conditions":54,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":62,"lastUpdatePostDateStruct":63,"startDateStruct":65,"completionDateStruct":66,"leadSponsor":68,"locationsCount":70},"100634172","deep-learning-framework-for-continuous-depth-of-anesthesia-forecasting-100634172","NCT07536230","Deep Learning Framework for Continuous Depth of Anesthesia Forecasting","Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia","Inclusion Criteria:\n\n* Patients scheduled for elective surgery requiring general anesthesia.\n* Procedures requiring continuous depth of anesthesia monitoring (BIS).\n\nExclusion Criteria:\n\n\\- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.",true,{"count":51,"type":21},115,"1 Day","The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states.\n\nWhile standard PK\u002FPD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.",[55,56,57,58,26,59,60,28],"BIS","BIS-EEG","Artifical Intelligence","Intraoperative","Anesthesia","Anesthesia Awareness","NOT_YET_RECRUITING","2026-04-10",{"date":64,"type":33},"2026-04-17",{"date":32,"type":21},{"date":67,"type":21},"2026-09-01",{"name":69,"class":40},"Universitair Ziekenhuis Brussel",1,{"id":72,"slug":73,"hasResults":11,"nctId":74,"briefTitle":75,"officialTitle":76,"acronym":77,"eligibilityCriteria":78,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":79,"enrollmentInfo":80,"targetDuration":4,"studyType":82,"phases":83,"briefSummary":85,"conditions":86,"keywords":98,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":102,"lastUpdatePostDateStruct":103,"startDateStruct":105,"completionDateStruct":107,"leadSponsor":109,"locationsCount":70},"100531998","the-fit-with-us-study-100531998","NCT06207084","The Fit With Us Study","Precision-based Teleexercise Trial to Increase Adherence in People With Disabilities","FITWITHUS","Eligible participants must meet the following criteria to be included: (1) live with a permanent physical disability that limits their mobility; (2) be 18 to 89 years of age; (3) have broadband internet access; (4) be able to speak and read English; and (5) be able to attend coaching and training sessions during the weekdays.\n\nParticipants are excluded from participation if they (1) possess significant visual acuity that prevents seeing a computer screen to follow a home exercise program; (2) have been regularly engaging in exercise for the previous 3 months; (3) are unable to use their arms and their legs at all to exercise; (4) are unsafe to exercise; (5) self-report that they are currently pregnant","89 Years",{"count":81,"type":21},257,"INTERVENTIONAL",[84],"NA","The purpose of this 32-week study is to use an innovative experimental design known as SMART (Sequential Multiple Assignment Randomized Trial), which will allow us to determine the best way to sequence the delivery of teleexercise (referred to as an adaptive intervention), combined with predictive analytics on participant adherence in a stepped program of physical activity interventions. All 257 participants will have access to a library of recorded video exercise content, and a weekly wellness article. Some participants will receive health coaching calls (1st randomization). Analytic data will be used to determine which participants are responding or not responding to the intervention. Participants not responding after 4 weeks will receive either live one-on-one or group exercise training (2nd randomization). After 8 weeks, the participant will receive only pre-recorded exercise content and articles for a 24-week maintenance phase (weeks 9-32). The study outcomes are: The effectiveness of the adaptive interventions; Exploring mediating and moderating variables; Sensitivity analysis of the predictive analytics.",[87,88,89,90,91,92,93,94,95,96,97,28],"Mobility Limitations","Disability Physical","Telehealth","Health Coaching","Physical Activity","Behavior, Health","eHealth","Health Literacy","Self Efficacy","Fatigue","Usability",[99,100,101],"telehealth","health coaching","disability","2026-01-07",{"date":104,"type":33},"2026-01-09",{"date":106,"type":33},"2024-09-01",{"date":108,"type":21},"2029-02-01",{"name":110,"class":40},"University of Alabama at Birmingham"]