Joint embedding-classifier learning for interpretable collaborative filtering.

Background: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion.

Results: We introduce the novel Joint Embedding Learning-classifier for improved Interpretability (JELI).

Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing.

Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities.

[Drug repositioning].

DRUG REPOSITIONING. Developing new therapeutic agents ("de novo") for human diseases is an increasingly lengthy and costly process, aimed at ensuring patient safety and drug efficacy. An alternative approach, drug repurposing or repositioning, involves using already commercialized or advanced molecules.

Experiences and Attitudes Toward Telemedicine in an Adult Congenital Heart Disease Clinic: Lessons Learned from the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has driven a broader adoption of telemedicine (TM). We aim to describe adult congenital heart disease (ACHD) patient experiences with TM and explore factors associated with positive attitude toward future TM visits. This is a cross-sectional, single-center study in an outpatient ACHD clinic from February to June, 2022.