Prescriptions

Explainable artificial intelligence for pharmacovigilance: What features are important when predicting adverse outcomes?

BACKGROUND AND OBJECTIVE: Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that take an individual's health information (e.g. their drug history and comorbidities) as inputs, and predict the probability that the individual will have...

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks.

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE. Using ...

Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data.

Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact...

An updated, computable MEDication-Indication resource for biomedical research.

The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To a...

Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention.

To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language process...

Persuasive robots should avoid authority: The effects of formal and real authority on persuasion in human-robot interaction.

Social robots must take on many roles when interacting with people in everyday settings, some of which may be authoritative, such as a nurse, teacher,...

Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning.

Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on th...

FluNet: An AI-Enabled Influenza-Like Warning System.

Influenza is an acute viral respiratory disease that is currently causing severe financial and resource strains worldwide. With the COVID-19 pandemic ...

A deep-learning framework for multi-level peptide-protein interaction prediction.

Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious pept...

Comprehensive Survey of Recent Drug Discovery Using Deep Learning.

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developi...

Precise Control of Customized Macrophage Cell Robot for Targeted Therapy of Solid Tumors with Minimal Invasion.

Injecting micro/nanorobots into the body to kill tumors is one of the ultimate ambitions for medical nanotechnology. However, injecting current micro/...

Predicting drug sensitivity of cancer cells based on DNA methylation levels.

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for dr...

A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction.

The application of anthropomorphic design features is widely assumed to facilitate human-robot interaction (HRI). However, a considerable number of st...

Intentions with actions: The role of intentionality attribution on the vicarious sense of agency in Human-Robot interaction.

Sense of Agency (SoA) is the feeling of control over one's actions and their consequences. In social contexts, people experience a "vicarious" SoA ove...

A machine learning framework for predicting drug-drug interactions.

Understanding drug-drug interactions is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing met...

Relation extraction from DailyMed structured product labels by optimally combining crowd, experts and machines.

The effectiveness of machine learning models to provide accurate and consistent results in drug discovery and clinical decision support is strongly de...

Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images.

Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quant...

Artificial Intelligence for Identifying the Prevention of Medication Incidents Causing Serious or Moderate Harm: An Analysis Using Incident Reporters' Views.

The purpose of this study was to describe incident reporters' views identified by artificial intelligence concerning the prevention of medication inci...

Synthesis of covalent bonding MWCNT-oligoethylene linezolid conjugates and their antibacterial activity against bacterial strains.

Nowadays, infectious diseases caused by drug-resistant bacteria have become especially important. Linezolid is an antibacterial drug active against cl...

From computer-aided drug discovery to computer-driven drug discovery.

Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discove...