Objective: How health researchers find secondary data to analyse is unclear. We sought to describe the approaches that UK organisations take to help researchers find data and to assess the findability of health data that are available for research.
Methods: We surveyed established organisations about how they make data findable. We derived measures of findability based on the first element of the FAIR principles (Findable, Accessible, Interoperable, Reproducible). We applied these to 13 UK health datasets and measured their findability via two major internet search engines in 2018 and repeated in 2021.
Results: Among 12 survey respondents, 11 indicated that they made metadata publicly available. Respondents said internet presence was important for findability, but that this needed improvement. In 2018, 8 out of 13 datasets were listed in the top 100 search results of 10 searches repeated on both search engines, while the remaining 5 were found one click away from those search results. In 2021, this had reduced to seven datasets directly listed and one dataset one click away. In 2021, Google Dataset Search had become available, which listed 3 of the 13 datasets within the top 100 search results.
Discussion: Measuring findability via online search engines is one method for evaluating efforts to improve findability. Findability could perhaps be improved with catalogues that have greater inclusion of datasets, field-level metadata and persistent identifiers.
Conclusion: UK organisations recognised the importance of the internet for finding data for research. However, health datasets available for research were no more findable in 2021 than in 2018.
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http://dx.doi.org/10.1136/bmjhci-2021-100325 | DOI Listing |
PLOS Digit Health
January 2025
Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Postnatal care refers to the support provided to mothers and their newborns immediately after childbirth and during the first six weeks of life, a period when most maternal and neonatal deaths occur. In the 30 countries studied, nearly 40 percent of women did not receive a postpartum care check-up. This research aims to evaluate and compare the effectiveness of machine learning algorithms in predicting postnatal care utilization in Ethiopia and to identify the key factors involved.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy.
Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge and treat all variables equally (uninformed machine learning). While the application of informed machine learning to bioinformatics and health informatics datasets has become more seamless, the likelihood of errors has also increased.
View Article and Find Full Text PDFInt J Cardiovasc Imaging
January 2025
Shanxi Cardiovascular Hospital, 18 Yifen Street, Taiyuan, 030024, Shanxi, China.
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality.
View Article and Find Full Text PDFJAMA Netw Open
December 2024
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis.
Importance: Identification of individuals at high risk of alcohol use disorder (AUD) and subsequent application of prevention and intervention programs has been reported to decrease the incidence of AUD. The polygenic score (PGS), which measures an individual's genetic liability to a disease, can potentially be used to evaluate AUD risk.
Objective: To assess the estimability and generalizability of the PGS, compared with family history and ADH1B, in evaluating the risk of AUD among populations of European ancestry.
Alzheimers Dement
December 2024
Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka, India.
Background: In the face of increasing Mild Cognitive Impairment(MCI) and Dementia rates among aging populations, understanding the factors shaping the non-normal cognitive decline is crucial. Leveraging the Clinical Dementia Rating (CDR) data, this study has a dual focus. (1) It utilizes CDR to aid early MCI diagnosis by investigating factors contributing to transition from Questionable- to Low- AD.
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