Prescriptions

Using natural language processing and machine learning to identify social needs in patient medical notes: model training with balanced versus real-world datasets.

OBJECTIVE: To evaluate natural language processing (NLP) and machine learning (ML) approaches for identifying social needs in electronic health record (EHR) notes, using balanced versus real-world imbalanced datasets. MATERIALS AND METHODS: A rule-based NLP framework using definite, non-negated keywords flagged social needs domains and "Any Social Need" in unstructured notes. Logistic regression, ...

Model Gateway: Management platform for model-driven drug discovery.

Pharmaceutical drug discovery demands machine learning (ML) infrastructure that goes beyond general-purpose Machine Learning Operations: inference-time composition of multiple models for multiparameter optimization, version management for physics-based models without serialized ML artifacts, enterprise compound library precomputation and governance structured around scientific organizational units...

Use of artificial intelligence for medication adherence assessment in patients with type 2 diabetes: an exploratory study in Morocco using ChatGPT and the validated general medication adherence scale.

OBJECTIVES: Medication adherence remains a major challenge in the management of type 2 diabetes (T2D), especially in middle-income countries such as M...

Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies.

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic impact of microbes on host cells and ther...

Artificial Intelligence-Enabled Electrocardiography for Monitoring Serum Potassium Dynamics in Patients With Severe Hypokalemia.

RATIONALE & OBJECTIVE: Severe hypokalemia requires prompt management and close surveillance. Although artificial intelligence-enabled electrocardiogra...

When doctors are not present: Understanding patients trying generative AI in the post-prescription stage.

BACKGROUND: When physicians are not physically present in patients' everyday illness management, individuals must continue making health-related decis...

Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.

AI-driven prediction of drug-target interaction (DTI) has emerged as a critical component in modern drug discovery and development. However, this appr...

Identifying Complementary Therapeutic Relationships for Drug Repurposing via Spectral-Spatial Graph Contrastive Learning.

Drug repurposing facilitates the discovery of novel therapies by identifying alternative indications for clinically approved drugs. However, many exis...

Short-Term Efficacy of the Artificial Intelligence HeartBot II in Increasing Awareness and Knowledge of Heart Attack in Women: Protocol for a Randomized Controlled Trial With a Waitlist Control.

BACKGROUND: Heart disease remains a leading cause of death for women in the United States. Despite this burden, awareness that heart disease is the le...

From voxel discovery to regional interaction: A multi-level interpretable framework for Alzheimer's disease diagnosis.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder where early diagnosis serves as the only viable window for effective interventi...

Pharmacogenomic and Drug Interactions Risk in Cardio-Oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a consid...

Machine learning-based prediction of postoperative restenosis risk following direct repair of paediatric coarctation of the aorta.

OBJECTIVES: Coarctation of the aorta is a congenital cardiovascular disease with focal aortic luminal narrowing, and paediatric patients face a high p...

Artificial intelligence in dermatologic pharmacokinetics: quantitative modeling, clinical translation, and optimization of precision therapeutics.

INTRODUCTION: Dermatology is rapidly transitioning from broad-spectrum therapies toward biologics, nanotechnology-based drug delivery, and precision t...

Opportunistic Screening for Chagas Disease Using an Artificial Intelligence-Enabled ECG: Prospective Evaluation of Feasibility and Diagnostic Accuracy.

BACKGROUND: Chagas disease (ChD), a neglected cardiovascular condition, affects 7.5 to 10.5 million people worldwide. Opportunistic screening during r...

Deciphering Anti-Cancer Drug Efficacy Through Nanomechanical Vibrations in Living Gastric Cancer Organoids.

Cancer poses a severe threat to human health. The limited sensitivity and efficiency of conventional drug screening methods create a pressing need for...

Assessing Different Machine Learning Algorithms to Identify Traumatic Cardiac Arrest.

OBJECTIVE: Traumatic cardiac arrest differs from non-traumatic regarding epidemiology. This study evaluated five machine learning classifiers' ability...

Real-world evidence on antibiotic use among privately insured beneficiaries in Saudi Arabia: a retrospective analysis of claims data.

OBJECTIVES: Inappropriate and broad-spectrum antibiotic use contributes to antimicrobial resistance (AMR) and higher healthcare costs. In Saudi Arabia...

Drug sensitivity prediction across cancer types using graph isomorphism networks and biological pathway features: A dual-branch deep learning approach.

Drug sensitivity prediction is an important issue within the precision medicine field. IC50, which is the molar drug dose needed to decrease the viabi...

Real-world analysis of depression-related adverse events associated with the adsorbed anthrax vaccine: integrating pharmacovigilance signals, machine learning risk prediction, and transcriptomic mechanisms.

BACKGROUND: Anthrax remains a life-threatening zoonotic disease in resource-limited settings. Adsorbed anthrax vaccine (AVA, BioThrax) is the only Uni...

Scalable and Generalizable Analog Design via Learning Medicinal Chemistry Intuition from Matched Molecular Pair Transformations.

Chemical analog design in the hit-to-lead and lead optimization stages of drug discovery relies on systematic structural modifications, often guided b...