4.5 Article

A Smart Healthcare Knowledge Service Framework for Hierarchical Medical Treatment System

Journal

HEALTHCARE
Volume 10, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/healthcare10010032

Keywords

smart healthcare management; hierarchical medical treatment system; knowledge service; case-based reasoning

Funding

  1. National Natural Science Foundation of China (NSFC) [71771077, 72071063, 62111530056]
  2. Fundamental Research Funds for the Central Universities [PA2020GDKC0020]
  3. Anhui Provincial Key Research and Development Plan [202004h07020016]
  4. Russian Foundation for Basic Research [21-57-53018]

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This paper uncovers the research hotspots and development directions of case-based reasoning in healthcare, proposing a framework and key technologies for medical knowledge service systems based on case-based reasoning in the big data environment. The study shows that the system, which provides case-based explanations for predicted results, has good interpretability and better acceptance compared to common intelligent decision support systems, supporting physicians in diagnosis, treatment, and teaching.
This paper reveals the research hotspots and development directions of case-based reasoning in the field of health care, and proposes the framework and key technologies of medical knowledge service systems based on case-based reasoning (CBR) in the big data environment. The 2124 articles on medical CBR in the Web of Science were visualized and analyzed using a bibliometrics method, and a CBR-based knowledge service system framework was constructed in the medical Internet of all people, things and data resources environment. An intelligent construction method for the clinical medical case base and the gray case knowledge reasoning model were proposed. A cloud-edge collaboration knowledge service system was developed and applied in a pilot project. Compared with other diagnostic tools, the system provides case-based explanations for its predicted results, making it easier for physicians to understand and accept, so that they can make better decisions. The results show that the system has good interpretability, has better acceptance than the common intelligent decision support system, and strongly supports physician auxiliary diagnosis and treatment as well as clinical teaching.

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