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Outcome Assessment, Health Care

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Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.

NeuroRehabilitation
BACKGROUND: Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait t...

Study of machine learning techniques for outcome assessment of leptospirosis patients.

Scientific reports
Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise cl...

Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods.

BMC medical research methodology
BACKGROUND: In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual's risk of e...

Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion.

Annals of clinical and translational neurology
OBJECTIVE: Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to d...

Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

Neurocritical care
BACKGROUND: The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional ou...

Phenotypes of Patients with Intracerebral Hemorrhage, Complications, and Outcomes.

Neurocritical care
BACKGROUND: The objective of this study was to define clinically meaningful phenotypes of intracerebral hemorrhage (ICH) using machine learning.

Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.

La Radiologia medica
Artificial intelligence (AI) has numerous applications in radiology. Clinical research studies to evaluate the AI models are also diverse. Consequently, diverse outcome metrics and measures are employed in the clinical evaluation of AI, presenting a ...

Prediction of pharmacological response in OCD using machine learning techniques and clinical and neuropsychological variables.

Spanish journal of psychiatry and mental health
INTRODUCTION: Obsessive compulsive disorder is associated with affected executive functioning, including memory, cognitive flexibility, and organizational strategies. As it was reported in previous studies, patients with preserved executive functions...

Effectiveness of robot-assisted training in adults with Parkinson's disease: a systematic review and meta-analysis.

Journal of neurology
AIM: This work aimed to update and summarize the existing evidence on the effectiveness of robot-assisted training (RAT) in adults with Parkinson's disease (PD).