Author(s)

Guangyu He

Corresponding Author

Guangyu He

Abstract

Medical data mining in the context of smart healthcare (abbreviated as medical data mining in the following text) refers to disciplines that focus on medical informatics, biostatistics, artificial intelligence, and other related knowledge related to medical data. It is a general term for information science and medical science and has distinct interdisciplinary characteristics and application orientation. Compared with a single discipline, it presents more complex attributes. It is impossible to quantify and apply traditional statistical models to achieve deeper exploration in the research, which limits the diversity of research methods. The academic community's attention to research methods for medical data mining is increasing with the arrival of big data and artificial intelligence. In recent years, scholars have emphasized the combination of innovation and interdisciplinary approaches, with machine learning technology construction and application in medical data mining being a typical representative. However, due to the complexity of the research objects and issues involved in this field, the academic community still needs to solve the problem of effectively integrating machine learning technology with medical data mining. This study attempts to explore the practical application of the above theories to provide a theoretical basis and practical reference for promoting the intersection and innovation of internal and external elements in the discipline.

Keywords

Smart healthcare; Medical data mining; Clinical decision-making; Machine learning