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Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk.

Scientific reports
This retrospective study leverages machine learning to determine the optimal timing for fracture reconstruction surgery in polytrauma patients, focusing on those with concomitant traumatic brain injury. The analysis included 218 patients admitted to ...

Machine learning based predictive model of the risk of Tourette syndrome with SHAP value interpretation: a retrospective observational study.

Scientific reports
Tourette syndrome is a relatively prevalent neurological condition, particularly among children, characterized by sudden, involuntary, repetitive movements or vocalizations. Contemporary diagnostic approaches for Tourette syndrome (TS) primarily rely...

Predicting cognitive frailty in community-dwelling older adults: a machine learning approach based on multidomain risk factors.

Scientific reports
Cognitive frailty (CF), a clinical syndrome involving both physical frailty (PF) and impaired cognition (IC), is associated with adverse health outcomes in older adults. This study aimed to identify key predictors of CF and develop a machine learning...

Feasibility and Effects of a Gait Assistance and Gait Resistance Training Program Using a Walking-Assist Wearable Robot for Community-Dwelling Older Adults: Single-Group, Pre-, and Posttest Study.

JMIR mHealth and uHealth
BACKGROUND: Two-thirds of people aged 65 years and older may require help with daily activities such as eating, bathing, and getting in and out of bed or a chair. Walking-assist wearable robots have shown significant improvements in physical function...

User Intent to Use DeepSeek for Health Care Purposes and Their Trust in the Large Language Model: Multinational Survey Study.

JMIR human factors
BACKGROUND: Generative artificial intelligence (AI)-particularly large language models (LLMs)-has generated unprecedented interest in applications ranging from everyday questions and answers to health-related inquiries. However, little is known about...

Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study.

JMIR medical education
BACKGROUND: Artificial intelligence (AI) systems are becoming increasingly relevant in everyday clinical practice, with Food and Drug Administration-approved AI solutions now available in many specialties. This development has far-reaching implicatio...

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study.

Journal of medical Internet research
BACKGROUND: Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early identification of SALI can improve patient outcomes; however, sepsis heterogeneity makes timely diagnosi...

Predicting the risk of ibrutinib in combination with R-ICE in patients with relapsed or refractory DLBCL using explainable machine learning algorithms.

Clinical and experimental medicine
Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) poses significant therapeutic challenges due to heterogeneous patient outcomes. This study aimed to evaluate the efficacy of the ibrutinib plus R-ICE regimen and to leverage explainable mac...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Scientific reports
Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer's lactate, and albumin, and to determine which patient factors are...

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.

Scientific reports
Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the non-linear relationships between hypoglycemia and its associated risk factors. The objective of this s...