Prescriptions

Predicting and Optimizing Synergistic Drug Combinations for Breast Cancer Treatment Using Machine Learning.

OBJECTIVES: The study aims to identify highly synergistic drug combinations for breast cancer treatment using machine learning models. The primary objective is to predict drug synergy scores accurately and rank combinations with the highest potential for therapeutic efficacy. METHODS: Machine learning models, including XGBoost, Random Forest (RF), and CatBoost (CB), were employed to analyze breast...

FEAOF: A transferable framework applied to prediction of hERG-related cardiotoxicity.

Inhibition of the hERG (human ether-a-go-go-related gene) channel by drug molecules can lead to severe cardiac toxicity, resulting in the withdrawal of many approved drugs from the market or halting their development in later stages. These findings highlight the pressing need to evaluate hERG blockade during drug development. We propose a novel framework for feature extraction and aggregation opti...

U-Net and YOLOv8 Artificial Intelligence Models for Automated Recognition of Internal Jugular Veins and Radial Arteries: A Foundational Study for Artificial Intelligence-guided Vascular Cannulation inPoint-of-care Ultrasound.

OBJECTIVES: This study compares the U-Net and You Only Look Once version 8 (YOLOv8) models for identifying internal jugular veins (IJVs) and radial ar...

Genetic predisposition to unwanted side effects under antidepressants and antipsychotics: a molecular-genetic study of 902 patients over 6 weeks.

This project aimed at (1) detailing the complex side effect patterns of 902 inpatients treated for major depression or schizophrenia under polypharmac...

Emerging innovations in ophthalmic drug delivery for diabetic retinopathy: a translational perspective.

Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes and a leading cause of vision impairment worldwide. Despite advancem...

Crafting an innovative one health-aligned machine learning framework for neglected tropical diseases elimination.

INTRODUCTION: Strategic investment in new interventions is crucial for controlling and eliminating NTDs. However, selecting the optimal intervention c...

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosi...

Self-Assembly MXene/PDA@Cotton Fabric Pressure Sensor Integrated with Deep Learning for Sign Language Recognition.

In recent years, smart textiles and flexible wearable products have garnered significant attention in fields such as human-computer interaction, medic...

Artificial Intelligence for Low-Dose CT Lung Cancer Screening: Comparison of Utilization Scenarios.

. Artificial intelligence (AI) tools for evaluating low-dose CT (LDCT) lung cancer screening examinations are used predominantly for assisting radiolo...

Predicting Drug-Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach.

BACKGROUND: Adverse drug reactions (ADRs) pose serious risks to patient health, and effectively predicting and managing them is an important public he...

AI based natural inhibitor targeting RPS20 for colorectal cancer treatment using integrated computational approaches.

The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying nov...

Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.

Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i....

EDRMM: enhancing drug recommendation via multi-granularity and multi-attribute representation.

BACKGROUND: Drug recommendation is a crucial application of artificial intelligence in medical practice. Although many models have been proposed to so...

Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model.

Intravenous immunoglobulin (IVIG) has been established as the first-line therapy for Kawasaki disease (KD). However, approximately 10%-20% of pediatri...

Enhancing diabetes risk prediction through focal active learning and machine learning models.

To improve the effectiveness of diabetes risk prediction, this study proposes a novel method based on focal active learning strategies combined with m...

Speech production as an artificial intelligence-based 'process' measure of cognition sensitive to mild cognitive impairment and Alzheimer's disease.

Process scores in neuropsychological tests add incremental validity for detecting non-normative cognitive aging trajectories. However, process scores...

Improving AI-Based Clinical Decision Support Systems and Their Integration Into Care From the Perspective of Experts: Interview Study Among Different Stakeholders.

BACKGROUND: Artificial intelligence (AI)-based systems are receiving increasing attention in the health care sector. While the use of AI is well advan...

Association between prenatal exposure to per- and polyfluoroalkyl substances and blood pressure among preschool-aged children: The moderating effect of child-age and the mediating effect of inflammatory cytokine.

Our aim is to evaluate the association of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) with offspring blood pressure (BP); examine ...

Bridging sustainable finance, AI, and clean technology amid economic shocks: How are they connected in median and extreme conditions?

We investigate the intricate relationships between sustainable markets, artificial intelligence (AI), and clean technology, focusing on their contribu...

Research on predicting the risk level of coal mine roof accident based on machine learning.

Coal mine roof accidents are one of the main types of accidents leading to the decline of coal mine safety productivity, accounting for about 20% of t...