Prescriptions

A distant supervision based approach to medical persona classification.

Identifying medical persona from a social media post is critical for drug marketing, pharmacovigilance and patient recruitment. Medical persona classification aims to computationally model the medical persona associated with a social media post. We present a novel deep learning model for this task which consists of two parts: Convolutional Neural Networks (CNNs), which extract highly relevant feat...

Effects of combining modern recovery techniques with neurotrophic medication and standard treatment in stroke patients.

Stroke is the main cause of disability after age 65, leaving survivors with sequels that require care and recovery treatment lasting years. It is estimated that by the year 2030 this pathology will be leading cause of mortality. To determine the efficacy of Lokomat training combined with neurotrophic medication and balneo-physiotherapeutic treatment in rehabilitation of post-stroke patients, a pro...

An adverse drug effect mentions extraction method based on weighted online recurrent extreme learning machine.

BACKGROUND AND OBJECTIVE: Automatic extraction of adverse drug effect (ADE) mentions from biomedical texts is a challenging research problem that has ...

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such dru...

Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein.

For efficient drug discovery and screening, it is necessary to simplify P-glycoprotein (P-gp) substrate assays and to provide in silico models that pr...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunother...

Applications of an interaction, process, integration and intelligence (IPII) design approach for ergonomics solutions.

This paper first reviews current ergonomics design approaches in delivering digital solutions to achieve a unified experience from interaction and bus...

Stochastic frontier analysis as knowledge-based model to improve sparing of organs-at-risk for VMAT-treated prostate cancer.

Stochastic frontier analysis (SFA) is used as a novel knowledge-based technique in order to develop a predictive model of dosimetric features from sig...

Serendipity-A Machine-Learning Application for Mining Serendipitous Drug Usage From Social Media.

Serendipitous drug usage refers to the unexpected relief of comorbid diseases or symptoms when taking medication for a different known indication. His...

Causability and explainability of artificial intelligence in medicine.

Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself an...

Simultaneous Estimation of Saxagliptin and Dapagliflozin in Human Plasma by Validated High Performance Liquid Chromatography - Ultraviolet Method.

OBJECTIVES: The fixed dose combination of saxagliptin and dapagliflozin is a recently approved antidiabetic medication. It is marketed under the brand...

Deep Collaborative Filtering for Prediction of Disease Genes.

Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve ...

Predicting drug-target interaction network using deep learning model.

BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find ne...

IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques.

Inverse Virtual Screening is a powerful technique in the early stage of drug discovery process. This technique can provide important clues for biologi...

LSTM Model for Prediction of Heart Failure in Big Data.

The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. W...

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a lar...

3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.

The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, mul...

A Generic Human-Machine Annotation Framework Based on Dynamic Cooperative Learning.

The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and coopera...

Machine learning on adverse drug reactions for pharmacovigilance.

Machine learning, especially deep learning, has the predictive power to predict adverse drug reactions, repurpose drugs and perform precision medicine...

Predicting protein-peptide interaction sites using distant protein complexes as structural templates.

Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and pot...