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A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.
Journal of medical Internet research
Jan 15, 2025
BACKGROUND: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.
Automated analysis of spoken language differentiates multiple system atrophy from Parkinson's disease.
Journal of neurology
Jan 15, 2025
BACKGROUND AND OBJECTIVES: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis o...
Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study.
Neuroinformatics
Jan 15, 2025
Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of b...
Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.
European radiology experimental
Jan 15, 2025
BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), i...
Development of a multi-laboratory integrated predictive model for silicosis utilizing machine learning: a retrospective case-control study.
Frontiers in public health
Jan 15, 2025
OBJECTIVE: Due to the high global prevalence of silicosis and the ongoing challenges in its diagnosis, this pilot study aims to screen biomarkers from routine blood parameters and develop a multi-biomarker model for its early detection.
Deep Learning Radiomics Nomogram Based on MRI for Differentiating between Borderline Ovarian Tumors and Stage I Ovarian Cancer: A Multicenter Study.
Academic radiology
Jan 14, 2025
RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics nomogram (DLRN) based on T2-weighted MRI to distinguish between borderline ovarian tumors (BOTs) and stage I epithelial ovarian cancer (EOC) preoperatively.
Interpretable machine learning models for COPD ease of breathing estimation.
Medical & biological engineering & computing
Jan 14, 2025
Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of...
Machine learning-based prediction of illness course in major depression: The relevance of risk factors.
Journal of affective disorders
Jan 14, 2025
BACKGROUND: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are stil...
The role of STEM teachers' emotional intelligence and psychological well-being in predicting their artificial intelligence literacy.
Acta psychologica
Jan 14, 2025
This study explores the role of emotional intelligence and psychological well-being in predicting artificial intelligence literacy among STEM teachers. A total of 383 Chinese STEM teachers from Henan, Zhejiang, and Yunnan provinces participated. The ...
Predicting delayed neurological sequelae in patients with carbon monoxide poisoning using machine learning models.
Clinical toxicology (Philadelphia, Pa.)
Jan 14, 2025
INTRODUCTION: Delayed neurological sequelae is a common complication following carbon monoxide poisoning, which significantly affects the quality of life of patients with the condition. We aimed to develop a machine learning-based prediction model to...
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