Doctor Specific Tag Recommendation for Online Medical Record Management

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Scopus Citations
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Author(s)

  • Shen Ge
  • Xian Wu
  • Tong Xu
  • Zhi Zheng

Detail(s)

Original languageEnglish
Title of host publicationKDD '23
Subtitle of host publicationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages5150–5161
ISBN (Print)979-8-4007-0103-0
Publication statusPublished - 2023

Conference

Title29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)
LocationLong Beach Convention & Entertainment Center
PlaceUnited States
CityLong Beach
Period6 - 10 August 2023

Abstract

With the rapid growth of online medical platforms, more and more doctors are willing to manage and communicate with patients via online services. Considering the large volume and various patient conditions, identifying and classifying patients’ medical records has become a crucial problem. To efficiently index these records, a common practice is to annotate them with semantically meaningful tags. However, manual labeling tags by doctors is impractical due to the possibility of thousands of tag candidates, which necessitates a tag recommender system. Due to the long tail distribution of tags and the dominance of low-activity doctors, as well as the unique uploaded medical records, this task is rather challenging. This paper proposes an efficient doctor specific tag recommendation framework for improved medical record management without side information. Specifically, we first utilize effective language models to learn the text representation. Then, we construct a doctor embedding learning module to enhance the recommendation quality by integrating implicit information within text representations and considering latent tag correlations to make more accurate predictions. Extensive experiment results demonstrate the effectiveness of our framework from the viewpoints of all doctors (20% improvement) or low-activity doctors (10% improvement). © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Research Area(s)

  • Tag Recommendation, Text Classification, Online Platform

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Doctor Specific Tag Recommendation for Online Medical Record Management. / Wang, Yejing; Ge, Shen; Zhao, Xiangyu et al.
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY: Association for Computing Machinery, 2023. p. 5150–5161.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review