Prescriptions

Iterative Dynamic Generic Learning for Face Recognition From a Contaminated Single-Sample Per Person.

This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisance facial variations in the wild, such as poor lightings, expression change, and disguises (e.g., wearing sunglasses, hat, and scarf). In SSPP-ce FR, ...

Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction.

Postoperative cognitive dysfunction (POCD) refers to the complications of the central nervous system before and after surgery in patients without mental disorders. Many studies have shown that surgical anesthesia may cause POCD, especially in elderly patients. This article aims to study the relationship between artificial intelligence-based general anesthetics and postoperative cognitive dysfuncti...

IL-8, MSPa, MIF, FGF-9, ANG-2 and AgRP collection were identified for the diagnosis of colorectal cancer based on the support vector machine model.

Colorectal cancer (CRC) is one of the most common cancer, and the early detection of CRC is essential to improve the survival rate of patients. To ide...

CEFEs: A CNN Explainable Framework for ECG Signals.

In the healthcare domain, trust, confidence, and functional understanding are critical for decision support systems, therefore, presenting challenges ...

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.

BACKGROUND: Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. ...

Deep generative neural network for accurate drug response imputation.

Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tu...

[G5 can't do without 5G].

In the Netherlands, 5 web-based systems co-exist in the public domain to provide relevant pharmacotherapeutic information for physicians, pharmacists ...

GanDTI: A multi-task neural network for drug-target interaction prediction.

Drug discovery processes require drug-target interaction (DTI) prediction by virtual screenings with high accuracy. Compared with traditional methods,...

Assessment of medication self-administration using artificial intelligence.

Errors in medication self-administration (MSA) lead to poor treatment adherence, increased hospitalizations and higher healthcare costs. These errors ...

Stereoelectronic effects in stabilizing protein-N-glycan interactions revealed by experiment and machine learning.

The energetics of protein-carbohydrate interactions, central to many life processes, cannot yet be manipulated predictably. This is mostly due to an i...

Target2DeNovoDrug: a novel programmatic tool for -deep learning based drug design for any target of interest.

The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design p...

Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning.

The US is experiencing an opioid epidemic, and opioid overdose is causing more than 100 deaths per day. Early identification of patients at high risk ...

Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.

BACKGROUND: Drug-induced QT prolongation is a potentially preventable cause of morbidity and mortality, however there are no widespread clinical tools...

Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach.

Drug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., D...

TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks.

With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in bo...

Trends in application of advancing computational approaches in GPCR ligand discovery.

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs...

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment.

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and molecule generation. However, these mo...

Convolutional neural network-based ambient light-independent panel digit surveillance technique for infusion pumps.

For effective patient therapy and improved patient safety, it is critical to administer medication accurately in accordance with doctor's prescription...

A Modular Design for Distributed Measurement of Human-Robot Interaction Forces in Wearable Devices.

Measurement of interaction forces distributed across the attachment interface in wearable devices is critical for understanding ergonomic physical hum...

DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation.

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced devel...