Text2DT: Decision rule extraction technology for clinical medical texts

Corresponding author: 王晓玲, xlwang@cs.ecnu.edu.cn

DOI: 10.12201/bmr.202211.00002

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  Abstract: The medical decision rules are often modelled as medical decision trees (MDTs), which are the core of a clinical decision support system. However, the current construction of MDTs relies heavily on time-consuming and laborious expert annotations, which hinders the construction, maintenance, and development of clinical decision support systems. This paper proposes a novel information extraction task: automatic extraction of MDTs from clinical medical texts. This paper constructs the first Text-to-MDT dataset, in which text refers to the medical text of clinical practice guidelines and medical textbooks that contain medical decision rules, and the MDTs model the medical decision rules in the text. Based on this dataset, this paper designs a decision tree extraction method and compares it with traditional methods, laying the foundation for the automatic extraction of MDTs.

  Key words: medical decision tree; natural language process; information extraction; deep learning

  Submit time: 9 November 2022

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2022-08-31	bmr.202211.00002V1	Download

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