Self-Reported Learning Strategies and Preferences in Health Informatics.

Despite the proliferation of educational programmes in Health Informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data.

Education Challenges Among Early Career Researchers in Medical Informatics.

There is limited knowledge about early career researchers' challenges when studying the interdisciplinary field of Medical Informatics (MI). We conducted a qualitative content analysis through semi-structured interviews with early career researchers in MI, including individuals pursuing Master's, PhD, and postdoctoral research programmes, across two higher education institutions in the UK. We identified five challenges, including understanding biological jargon, interpreting biological data, interdisciplinary communication, understanding mathematical/statistical concepts, and programming difficulties.

Identifying Learning Preferences and Strategies in Health Data Science Courses: Systematic Review.

Background: Learning and teaching interdisciplinary health data science (HDS) is highly challenging, and despite the growing interest in HDS education, little is known about the learning experiences and preferences of HDS students.

Objective: We conducted a systematic review to identify learning preferences and strategies in the HDS discipline.

Methods: We searched 10 bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, Wiley Online Library, ScienceDirect, SpringerLink, EBSCOhost, ERIC, and IEEE Xplore) from the date of inception until June 2023.

Providing insights into health data science education through artificial intelligence.

Background: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students' learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs.

DisCoVering potential candidates of RNAi-based therapy for COVID-19 using computational methods.

The ongoing pandemic of a novel coronavirus (SARS-CoV-2) leads to international concern; thus, emergency interventions need to be taken. Due to the time-consuming experimental methods for proposing useful treatments, computational approaches facilitate investigating thousands of alternatives simultaneously and narrow down the cases for experimental validation. Herein, we conducted four independent analyses for RNA interference (RNAi)-based therapy with computational and bioinformatic methods.

Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity.

Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs.