AI Medical Compendium

BiGranMolNet: A deep learning method for predicting blood-brain barrier permeability based on Bi-Granularity Molecular Graphs.

Journal of biomedical informatics Jul 18, 2026

OBJECTIVE: The selective permeability of the blood-brain barrier (BBB) hinders the delivery of central nervous system (CNS) drugs to brain targets. It is therefore crucial to determine BBB permeability during CNS drug development. METHODS: We propose... read more

Utilizing Large Language Models to Enhance Patient-Reported Outcome Measures: Application to the EQ-5D-5L and Bolt-ons.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research Jul 18, 2026

OBJECTIVES: Large language models (LLMs) may be useful tools for the development/adaptation of patient-reported outcome measures (PROMs). As a methodological proof-of-concept, we evaluated the use of LLMs to support the identification of potential EQ... read more

SMFF-Net: Spatiotemporal-frequency Multi-domain Feature Fusion Network for EEG-based brain state detection.

Neuroscience Jul 18, 2026

The timely and reliable detection of driver fatigue is crucial for reducing driving risks and improving traffic safety. EEG-based deep learning approaches for brain state detection suffer from insufficient cross-domain feature interaction and limited... read more

Association of Histological Features of Cortical and Medullary Peritubular Capillaries With Progressive CKD.

American journal of kidney diseases : the official journal of the National Kidney Foundation Jul 18, 2026

RATIONALE & OBJECTIVE: Peritubular capillary (PTC) rarefaction occurs in chronic kidney disease (CKD), but the independent association of PTC histological features with CKD progression is uncertain. Assessment of this relationship was the principal a... read more

PatientSpace: A multimodal graph-based latent representation framework for modeling neurodegenerative disease heterogeneity.

NeuroImage Jul 18, 2026

Neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial biological and clinical heterogeneity, complicating diagnosis, subtype characterization, and prediction of disease progression. We intro... read more

Model Development and Feasibility of Real-World Deployment of Multimodal Input-Based Subtyping of Depression in Tele-Counseling for Scalable Mental Health Assessment.

Bio Systems Jul 18, 2026

The rapid growth of tele-counseling and the use of lay counselors in high-volume, low-resource mental health services has created a need for scalable tools for early detection and triage. Effective personalization now requires stratifying individuals... read more

Bridging the Vaccine Gap: Scientific and Technological Advances for Diseases Lacking Effective Vaccines.

Antiviral research Jul 18, 2026

Despite remarkable achievements in infectious disease control, more than 20 major pathogens responsible for significant global morbidity and mortality remain without licensed, effective vaccines. This so-called 'vaccine gap' disproportionately burden... read more

Clinically meaningful risk factors for recurrence in T1 colorectal cancer treated with endoscopic resection alone identified by unsupervised machine learning: a multicenter study.

Endoscopy Jul 18, 2026

BACKGROUND AND STUDY AIM: Identifying high-risk patients for recurrence after endoscopic resection (ER) of T1 colorectal cancer (CRC) remains challenging. This study aimed to identify recurrence-risk subtypes and develop an interpretable risk stratif... read more

MAGMED: A clinically grounded LLM-guided synthetic augmentation framework for admission-time ICU mortality prediction.

Computers in biology and medicine Jul 18, 2026

BACKGROUND: Mortality risk prediction for elderly intensive care unit (ICU) patients with severe infections remains challenging due to limited sample sizes and poor generalization across institutions. Existing models often fail to adequately address ... read more

Enhanced multimodal MRI classification of schizophrenia through cross-attention graph neural networks.

Medical image analysis Jul 18, 2026

We propose TB-GCAN, a tri-branch cross-attention graph neural network for schizophrenia classification using multimodal MRI, including sMRI, fMRI, and DTI. Built on a multi-site dataset of 1191 samples from seven scanning sites, the model exploits at... read more

Artificial Intelligence Medical Compendium