BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypoth...
International journal of medical informatics
Oct 3, 2019
INTRODUCTION: Clinicians commonly use the modified Rankin Scale (mRS) and the Barthel Index (BI) to measure clinical outcome after stroke. These are potential targets in machine learning models for stroke outcome prediction. Therefore, the quality of...
RATIONALE: Multiple clinical trials support the effectiveness of cardiac resynchronization therapy (CRT); however, optimal patient selection remains challenging due to substantial treatment heterogeneity among patients who meet the clinical practice ...
Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past ...
Journal of neuroengineering and rehabilitation
Jul 29, 2019
BACKGROUND: Balance impairments are common in patients with infratentorial stroke. Although robot-assisted gait training (RAGT) exerts positive effects on balance among patients with stroke, it remains unclear whether such training is superior to con...
BACKGROUND: Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PG...
PURPOSE: Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating...
BACKGROUND: In mental health, outcomes are currently measured by changes of individual scores. However, such an analysis on individual scores does not take into account the interaction between symptoms, which could yield crucial information while inv...
We evaluate the potential of connectionist models of developmental disorders to offer insights into the efficacy of interventions. Based on a range of computational simulation results, we assess factors that influence the effectiveness of interventio...
BACKGROUND: Recent advances in machine learning have given rise to deep learning, which uses hierarchical layers to build models, offering the ability to advance value-based healthcare by better predicting patient outcomes and costs of a given treatm...
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