Prescriptions

FSSM-DDI: Fusion State Space Model for predicting drug-drug interaction using social-media and drug descriptions.

BACKGROUND: Predicting drug-drug interactions (DDIs) from social-media and drug descriptions is crucial for healthcare, drug regulation, and pharmaceutical research, yet remains a challenging task. Existing deep learning and Transformer-based approaches often struggle with modeling long-range dependencies within sentences or entail high computational costs, limiting their practical applicability i...

Precision Medicine Gene Network Analyser: part I-cancer driver gene identification through network topology and ensemble machine learning.

PURPOSE: Precision oncology depends on identifying cancer driver genes and linking them to targeted therapies. Current methods using curated gene sets or generic classifiers often miss biologically relevant patterns in complex gene interaction networks. METHODS: We developed the Precision Medicine Gene Network Analyser, integrating network topology analysis with machine learning for cancer gene id...

Ontology-Enhanced Deep Learning for Mechanistic Prediction of Drug-Drug Interactions: A Clinically Interpretable Framework.

Drug-drug interactions (DDIs) have critical impacts on patient safety and healthcare efficiency because of their significant contributions to adverse ...

Participatory Digital Twins for Chronic Care: From Predictive Models to Shared Sensemaking.

Health digital twins, computational models that integrate longitudinal data, simulation, and forecasting, are increasingly proposed as tools for chron...

HKD-CPI: high-order knowledge distillation enhanced inductive compound-protein interaction prediction.

MOTIVATION: Accurately identifying compound-protein interactions (CPIs) is critical for accelerating drug discovery. Recent deep learning methods have...

Reproducibility, validation, and failure modes across classical and AI-driven molecular docking.

Molecular docking is indispensable across computer‑aided discovery. However, its conclusions often hinge more on modeling choices than on software bra...

Human and Robot Assistance for Cognitive Load in Younger and Older Adults: Multimodal Within-Subject Experimental Study.

BACKGROUND: Maintaining cognitive efficiency and independence is a central goal of healthy aging. Socially assistive robots (SARs) are increasingly pr...

CM-MTL-DTI: Drug-Target Interaction Prediction via Cross-Modal Alignment and Multi-Task Learning.

Drug-target interaction (DTI) prediction is critical for candidate compound screening and elucidation of mechanisms of action in drug discovery and re...

From Parasite to Pill: Harnessing Biology for Breakthroughs in Antimalarial Drug Discovery.

The global imperative for malaria eradication demands innovative strategies for antimalarial drug discovery, particularly in the face of growing drug ...

Carfentanil in the illicit drug supply: A machine learning approach for trace detection using surface-enhanced Raman spectroscopy.

BACKGROUND: Carfentanil is an extremely potent synthetic fentanyl analogue often present at trace levels alongside other fentanyl analogues and long-a...

Investigating the utilization of LIME-enhanced AI and generated data for evaluating the risks associated with radial artery puncture procedures.

Radial artery puncture, a routine arterial cannulation procedure for perioperative and critical care settings, is limited by high first-attempt failur...

A critical evaluation of generative query expansion on biomedical literature retrieval.

OBJECTIVE: To evaluate the effectiveness of generative query expansion for biomedical literature retrieval. MATERIALS AND METHODS: We thoroughly exami...

Predicting pharmacy choice for managed care network design.

BACKGROUND: Pharmacy type selection is a key component of medication access and use. Prior studies have commonly used logistic regression to examine p...

Machine learning evaluation of racial and ethnic inclusion under alternative cost-based approaches to Medicare Medication Therapy Management eligibility.

BACKGROUND: The eligibility framework for the Medicare Medication Therapy Management (MTM) program has been associated with a lower likelihood of meet...

Numerical Inverse Design of Patient-Specific Dental Implants: Accelerating FEA-Based Optimization via Evolutionary Neural Surrogates.

Mechanical complications in dental implantology often arise from a mismatch between standardized geometries and patient-specific anatomical constraint...

Gallstone disease classification using SLOA-optimized CatBoost classifier with explainable AI.

Gallstones are small stones that form in the gallbladder. Around 80% of individuals with gallstones do not present any symptoms. Despite the high accu...

Does lens opacity matter? The effect of cataract on deep learning based cardiovascular disease risk scores from fundus photos.

PURPOSE: To investigate the effect of cataracts on a deep learning (DL) model for cardiovascular disease (CVD) risk prediction. METHODS: This retrospe...

AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: A loperamide case study.

Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays...

Prevalence and Predictors of Non-Exclusive Breastfeeding at Hospital Discharge in Uruguay.

BACKGROUND: The prescription of infant formula during postpartum hospitalization is one of several factors that influence breastfeeding. RESEARCH AIMS...

Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.

The molecular and spatial heterogeneity of gliomas severely limits accurate prediction of postoperative adjuvant chemotherapy efficacy, representing a...