Machine learning in healthcare refers to the application of computational algorithms that can learn from and make predictions or decisions based on medical data without being explicitly programmed for each specific task. Research published in Medical Informatics and Decision Making on this topic addresses critical c…
Machine learning in healthcare refers to the application of computational algorithms that can learn from and make predictions or decisions based on medical data without being explicitly programmed for each specific task. Research published in Medical Informatics and Decision Making on this topic addresses critical clinical applications including the development of prognostic models for cancer outcomes and the standardization of adverse drug event reporting systems. Published work has examined machine learning approaches for predicting triple negative breast cancer histological grading and lymph node metastasis using clinical variables, demonstrating how algorithmic analysis can support oncological decision-making. The journal has also explored foundational challenges in implementing machine learning systems, particularly the need for standardized definitions, documentation practices, and data mapping protocols for adverse drug events—work that addresses essential prerequisites for training reliable clinical algorithms. This research matters because effective machine learning applications in healthcare depend both on sophisticated analytical methods and on the quality, consistency, and interoperability of underlying medical data structures, making contributions in both algorithm development and data standardization essential for advancing the field.
2 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 2 articles above have been cited 8 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
A sample of recent works citing this journal's research on Machine Learning in Healthcare, linking to each citing work.