Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with clinical routine data have only resulted in moderate prediction accuracies. Neuroimaging data showe...
Annals of agricultural and environmental medicine : AAEM
39743720
INTRODUCTION AND OBJECTIVE: Surface electromyography (sEMG) measurements are a valid method for sublesional muscle activity following spinal cord injury (SCI). In the literature there are few reports evaluating the effect of robotic assisted gait tra...
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from...
BACKGROUND AND PURPOSE: Mechanical Thrombectomy (MT) has recently become the standard of care for anterior circulation stroke with large vessel occlusion, but predictive factors of successful MT are still not clearly defined. To tailor treatment indi...
BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategie...
BMC medical informatics and decision making
39962480
BACKGROUND: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges re...
BACKGROUND: Migraine is a complex neurological disorder with significant clinical variability, posing challenges for effective management. Multiple treatments are available for migraine, but individual responses vary widely, making accurate predictio...
BACKGROUND: Over the last 10-15 years, US health care and the practice of medicine itself have been transformed by a proliferation of digital medicine and digital therapeutic products (collectively, digital health tools [DHTs]). While a number of DHT...
Journal of the American Medical Informatics Association : JAMIA
39775871
The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance...
Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized clinical trials in major depressive disorder (MDD). The objective of this study is to develop artificial neural network (ANN) models to predict the indi...