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BMC Medical Informatics and Decision Making is a peer-reviewed journal published by BMC (BioMed Central), covering Electronic Health Records Systems, Machine Learning in Healthcare, Mobile Health and mHealth Applications. Over its lifetime it has published 4,502 papers, which have received 147,047 citations.
| Publisher | BMC (BioMed Central) |
|---|---|
| Reference style | Numbered (Vancouver) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C. A representative article title. BMC Medical Informatics and Decision Making. 2023;12(3):45–58.
Formats any DOI in BMC Medical Informatics and Decision Making style. No sign-up. |
| Publishes research in | Electronic Health Records Systems Machine Learning in Healthcare Mobile Health and mHealth Applications Biomedical Text Mining and Ontologies Patient-Provider Communication in Healthcare |
| ISSN | 1472-6947 |
| Citation impact (2-yr) | 5.98 |
| h-index | 145 |
| i10-index | 2,673 |
| Article processing charge | $1,925 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | University of Toronto |
| Journal website | bmcmedinformdecismak.biomedcentral.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
BACKGROUND: Supporting 21st century health care and the practice of evidence-based medicine (EBM) requires ubiquitous access to clinical information and to knowledge-based resources to answer clinical questions. Many questions go unanswered, however, due to lack of skills in formulating questions, crafting effective search strategies, and accessing databases to identify best levels of evidence. METHODS: This…
BACKGROUND: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes…
BACKGROUND: Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study ai7ms to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction.…
BACKGROUND: Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require…
BACKGROUND: Nowadays more and more clinical guidelines for health care professionals are being developed. However, this does not automatically mean that these guidelines are actually implemented. The aim of this meta-review is twofold: firstly, to gain a better understanding of which factors affect the implementation of guidelines, and secondly, to provide insight into the "state-of-the-art"…
