Prognosticating Prosthetic Ambulation Ability in People With Lower Limb Amputation in Early Post-operative Phase.

Objective: To formulate a prognostication model in the early post-operation phase of lower limb amputation to predict patient's ability to ambulate with a prosthesis post rehabilitation.

Design: Retrospective cohort study, using data collected from electronic medical records. Predictive factors and prosthetic ambulation outcomes post rehabilitation were used to develop prognostic models via machine learning techniques.

Risk Prediction of Diabetic Foot Amputation Using Machine Learning and Explainable Artificial Intelligence.

Background: Diabetic foot ulcers (DFUs) are serious complications of diabetes which can lead to lower extremity amputations (LEAs). Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications.

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients.

Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability.