AIMC Topic: Humans

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Predicting All-Cause Mortality in Diabetic Patients 2 Years in Advance Using Aggregated EHR Data and Machine Learning.

Journal of medical systems
This study presents a machine learning-driven model predicting all-cause mortality two years in advance using administrative health data focused on diabetic patients. Integrating hospitalization records, emergency department data, demographics, and c...

Biomaterials for CNS disorders: a review of development from traditional methods to AI-assisted optimization.

Journal of materials science. Materials in medicine
Treating neurodegenerative and traumatic brain disorders is profoundly challenging due to factors like permanent tissue loss and the restrictive nature of the Blood-Brain Barrier (BBB), which limits drug delivery to the brain. Biomaterials offer a pr...

Explainable machine learning algorithm predicting working memory performance in Parkinson's disease using task-fMRI.

Journal of neurology
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions, particularly working memory (WM). Machine learning offers an advantage for decoding complex brain activity patterns, but its applica...

Quality assessment of patient-facing urologic telesurgery content using validated tools.

Journal of robotic surgery
INTRODUCTION: With increasing accessibility to Artificial Intelligence (AI) chatbots, the precision and clarity of medical information provided require rigorous assessment. Urologic telesurgery represents a complex concept that patients will investig...

Correspondence regarding "Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients".

World journal of urology
Zhao et al. present machine-learning models to predict intraoperative hemodynamic instability in hypertensive pheochromocytoma and paraganglioma surgery. The clinical motivation is sound and the reported discrimination and decision-curve metrics indi...

Remote patient monitoring system combining hardware and artificial intelligence based software.

Biomedical physics & engineering express
This study details the development of a remote patient monitoring system with a primary focus on a novel, customized Deep Neural Network (DNN) for arrhythmia detection. The system integrates hardware for real-time data collection from biomedical sens...

Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.

JMIR formative research
BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into clinical diagnostics; yet, its lack of transparency hinders trust and adoption among health care professionals. The explainable artificial intelligence (XAI) has the poten...

Brain-to-text decoding with context-aware neural representations and large language models.

Journal of neural engineering
. Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermedia...

Clinical implementation of an AI-enabled ECG for hypertrophic cardiomyopathy detection.

Heart (British Cardiac Society)
BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often underdiagnosed. Artificial intelligence (AI)-based notification of HCM suspicion on a 12-lead ECG has been proposed to assist patient identification and evaluation. However, there has been no stu...

Deep learning detection of dynamic exocytosis events in fluorescence TIRF microscopy.

PLoS computational biology
Segmentation and detection of biological objects in fluorescence microscopy is of paramount importance in cell imaging. Deep learning approaches have recently shown promise to advance, automatize and accelerate analysis. However, most of the interest...