Prescriptions

Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems.

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representat...

DrugBank 6.0: the DrugBank Knowledgebase for 2024.

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and qua...

Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.

Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (I...

Enhancing Patient Medication Safety at Home: A Patient-Facing Technology Architecture Integrating REDCap, Visualization Dashboards, and an AI- Driven Chatbot.

This work demonstrates a novel architecture for a patient-facing technology (PFT) that supports patients with cancer in self-managing medication conce...

Optimizing Medication Querying Using Ontology-Driven Approach with OMOP: with an application to a large-scale COVID-19 EHR dataset.

Efficient querying for medication information in Electronic Health Record (EHR) datasets is crucial for effective patient care and clinical research. ...

Enhancing Semantic and Structure Modeling of Diseases for Diagnosis Prediction.

Electronic Health Records (EHRs) are valuable healthcare data, aiding researchers and doctors in improving diagnosis accuracy. Researchers have develo...

A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.

Mapping electronic health records (EHR) data to common data models (CDMs) enables the standardization of clinical records, enhancing interoperability ...

Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes.

This study compared large language models (LLMs) and Bidirectional Encoder Representations from Transformers (BERT) models in identifying medication n...

Current Scenario and Future Prospects of Adverse Drug Reactions (ADRs) Monitoring and Reporting Mechanisms in the Rural Areas of India.

Pharmacovigilance (PV) deals with the detection, collection, assessment, understanding, and prevention of adverse effects associated with drugs. The o...

Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

BACKGROUND: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various ty...

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.

Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins...

Accelerating COVID-19 Drug Discovery with High-Performance Computing.

The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has t...

Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enha...

Robotic Pills as Innovative Personalized Medicine Tools: A Mini Review.

The most common route for drug administration is the oral route due to the various advantages offered by this route, such as ease of administration, c...

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including ...

Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.

Currently, the main therapeutic methods for cancer include surgery, radiation therapy, and chemotherapy. However, chemotherapy still plays an importan...

Evaluation of inpatient medication guidance from an artificial intelligence chatbot.

PURPOSE: To analyze the clinical completeness, correctness, usefulness, and safety of chatbot and medication database responses to everyday inpatient ...

Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.

Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a ...

Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning.

BACKGROUND AND PURPOSE: Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatri...

Differences in Social Expectations About Robot Signals and Human Signals.

In our daily lives, we are continually involved in decision-making situations, many of which take place in the context of social interaction. Despite ...