AIMC Topic: Treatment Outcome
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Video-based assessments of activities of daily living: generating real-world evidence in pediatric rare diseases.
Expert review of pharmacoeconomics & outcomes research
Jun 7, 2024
INTRODUCTION: Preserving function and independence to perform activities of daily living (ADL) is critical for patients and carers to manage the burden of care and improve quality of life. In children living with rare diseases, video recording ADLs o...
Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study.
Scientific reports
Jun 7, 2024
Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis ...
Multi-machine Learning Model Based on Habitat Subregions for Outcome Prediction in Adenomyosis Treated by Uterine Artery Embolization.
Academic radiology
Jun 6, 2024
RATIONALE AND OBJECTIVES: To establish and validate a predictive multi-machine learning model for the long-term efficacy of uterine artery embolization (UAE) in the treatment of adenomyosis based on habitat subregions.
Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus.
Neurosurgery
Jun 6, 2024
BACKGROUND AND OBJECTIVES: Ventriculo-peritoneal shunt procedures can improve idiopathic normal pressure hydrocephalus (iNPH) symptoms. However, there are no automated methods that quantify the presurgery and postsurgery changes in the ventricular vo...
Effects of robot-assisted upper limb training combined with functional electrical stimulation in stroke patients: study protocol for a randomized controlled trial.
Trials
Jun 4, 2024
INTRODUCTION: About 17-80% stroke survivors experience the deficit of upper limb function, which strongly influences their independence and quality of life. Robot-assisted training and functional electrical stimulation are commonly used interventions...
Humans
Female
Aged
Time Factors
Treatment Outcome
Recovery of Function
Randomized Controlled Trials as Topic
Robotics
Adult
Male
Middle Aged
Quality of Life
Activities of Daily Living
Hemiplegia
Upper Extremity
Electric Stimulation Therapy
Stroke Rehabilitation
Single-Blind Method
Stroke
Combined Modality Therapy
Exercise Therapy
Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.
Diabetes, obesity & metabolism
Jun 3, 2024
AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D.
Surgical factors play a critical role in predicting functional outcomes using machine learning in robotic-assisted total knee arthroplasty.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
May 31, 2024
PURPOSE: Predictive models help determine predictive factors necessary to improve functional outcomes after total knee arthroplasty (TKA). However, no study has assessed predictive models for functional outcomes after TKA based on the new concepts of...
Predicting Chronic Pain and Treatment Outcomes Using Machine Learning Models Based on High-dimensional Clinical Data From a Large Retrospective Cohort.
Clinical therapeutics
May 31, 2024
PURPOSE: To identify factors and indicators that affect chronic pain and pain relief, and to develop predictive models using machine learning.
Machine Learning from Veno-Venous Extracorporeal Membrane Oxygenation Identifies Factors Associated with Neurological Outcomes.
Lung
May 30, 2024
BACKGROUND: Neurological complications are common in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO) support. We used machine learning (ML) algorithms to identify predictors for neurological outcomes for these patients.
A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis.
Asian journal of psychiatry
May 28, 2024
BACKGROUND: In order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment.
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