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Fast Healthcare Interoperability Resources (FHIR) for Interoperability in Health Research: Systematic Review

Carina Nina Vorisek, Moritz Lehne, Sophie Anne Ines Klopfenstein, Paula Josephine Mayer et al. • JMIR Medical Informatics (2022)

BackgroundThe standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficien...

DOAJ Open Access (DOAJ) DOI: 10.2196/35724

Digital quality gradient and the burden of elderly depression: A spatial multi kernel SVR analysis of socio economically vulnerable groups in England

Jin Rui, Yijing Li, Hector D. Menendez, Mariana Pinto da Costa • Digital Health (2026)

Digital infrastructure is increasingly understood as part of the contemporary built environment and as a policy-relevant area-level correlate of older adults’ mental health. Existing studies have often relied on linear models and have paid ...

DOAJ Open Access (DOAJ) DOI: 10.1177/20552076261465140

Effects of chronic taurine administration on healthy pregnant rats and the consequences on the offspring: Datasets for motor tests and oxidative stress

Viridiana Vargas-Castro, Ricardo Gomez-Diaz, Victor M. Blanco-Alvarez, Constantino Tomas-Sanchez et al. • Data in Brief (2022)

We present the data for taurine (2-aminoethanesulfonic acid) treatment to healthy pregnant Sprague Dawley rats (SD). At embryonic day 15 (E15), healthy pregnant SD rats were given taurine treatment (50 mg/L drinking water) and then to their...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.dib.2022.108015

Injury death certificates without specification of the circumstances leading to the fatal injury – the Norwegian Cause of Death Registry 2005–2014

Christian Lycke Ellingsen, Marta Ebbing, G. Cecilie Alfsen, Stein Emil Vollset • Population Health Metrics (2018)

Abstract Background For injury deaths, the underlying cause of death is defined as the circumstances leading to the injury. When this information is missing, the ICD-10 code X59 (Exposure to unspecified factor) is used. Lack of knowledge of...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12963-018-0176-2

Electronic Cigarette Use Is Not Associated with COVID-19 Diagnosis

Thulasee Jose, Ivana T. Croghan, J. Taylor Hays, Darrell R. Schroeder et al. • Journal of Primary Care & Community Health (2021)

This analysis tested the hypothesis that current e-cigarette use was associated with an increased risk of SARS-CoV-2 infection in patients seeking medical care. E-cigarette and conventional cigarette use were ascertained using a novel elect...

DOAJ Open Access (DOAJ) DOI: 10.1177/21501327211024391

Behavioral dataset for Long-Evans and its schizophrenia-like substrain through several generations

Gábor Kőrösi, Oliver Czimbalmos, Gabriella Kekesi, Gyongyi Horvath • Scientific Data (2026)

Abstract We present a high-throughput behavioral dataset acquired with Ambitus, an automated reward-based corridor system that records locomotor and exploratory activities and cognitive functions after minimal handling. The collection conta...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41597-026-06735-0

Modelling the effect of subcellular mutations on the migration of cells in the colorectal crypt

Lotte B. Romijn, Axel A. Almet, Chin Wee Tan, James M. Osborne • BMC Bioinformatics (2020)

Abstract Background Many cancers arise from mutations in cells within epithelial tissues. Mutations manifesting at the subcellular level influence the structure and function of the tissue resulting in cancer. Previous work has proposed how ...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12859-020-3391-3

Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs

Kai-Chih Pai, Wen-Cheng Chao, Yu-Len Huang, Ruey-Kai Sheu et al. • Digital Health (2022)

Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method The transfer learning method w...

DOAJ Open Access (DOAJ) DOI: 10.1177/20552076221120317

Development and validation of an interpretable machine learning model for predicting the risk of coronary heart disease risk in diabetes mellitus patients: a dual-center retrospective study

Ye Kuang, Yan Yu, Jing Li, Chuanmei Peng et al. • BMC Medical Informatics and Decision Making (2026)

Abstract Background Diabetes mellitus (DM) combined with coronary heart disease (CHD) significantly increases the risk of cardiovascular events with a greater mortality rate. Therefore, establishing a predictive model can help DM patients r...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-026-03461-w

An Integrated Approach for Designing and Analyzing Lumbar Vertebral Biomodels with Artificial Disc Replacement

Mhd Ayham Darwich, Katreen Ebrahem, Maysaa Shash, Hasan Mhd Nazha et al. • Applied Mechanics (2023)

This study aims to develop an integrated approach for 3D lumbar vertebral biomodel design and analysis, specifically targeting unilevel disc degeneration and the replacement of lumbar artificial discs. Key objectives include improving exist...

DOAJ Open Access (DOAJ) DOI: 10.3390/applmech4040063

Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study

Yongtai Liu, Zhijun Yin, Congning Ni, Chao Yan et al. • Journal of Medical Internet Research (2023)

BackgroundBy the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic s...

DOAJ Open Access (DOAJ) DOI: 10.2196/42985

Theory, Abstraction and Design in Medical Informatics

F. Martín, J Crespo, Holger Billhardt, Víctor Maojo • Methods of Information in Medicine (2002)

OBJECTIVE: To analyze the scientific and engineering components of Medical Informatics. A clear characterization of these components should be undertaken to categorize different areas of Medical Informatics and create a research agenda for ...

OPENALEX Open Access DOI: https://doi.org/10.1055/s-0038-1634312

Exploring feature selection and classification methods for predicting heart disease

Robinson Spencer, Fadi Thabtah, Neda Abdelhamid, Michael Thompson • Digital Health (2020)

Machine learning has been used successfully to improve the accuracy of computer-aided diagnosis systems. This paper experimentally assesses the performance of models derived by machine learning techniques by using relevant features chosen b...

DOAJ Open Access (DOAJ) DOI: 10.1177/2055207620914777

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

Daigle Bernie J, Roh Min K, Petzold Linda R, Niemi Jarad • BMC Bioinformatics (2012)

Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data i...

DOAJ Open Access (DOAJ) DOI: 10.1186/1471-2105-13-68

Integrating machine learning algorithms and explainable artificial intelligence approach for predicting patient unpunctuality in psychiatric clinics

Alireza Kasaie, Suchithra Rajendran • Healthcare Analytics (2023)

This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.health.2023.100242

Massive Open Online Courses (MOOCs): Data on higher education

Waleed Al-Rahmi, Ahmed Aldraiweesh, Noraffandy Yahaya, Yusri Bin Kamin et al. • Data in Brief (2019)

The data presented in this article are based on provides a systematic and organized review of 219 studies regarding using of Massive Open Online Courses (MOOCs) in higher education from 2012 to 2017. Consequently, the extant, peer-reviewed ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.dib.2018.11.139