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Doctor Specific Tag Recommendation for Online Medical Record Management

Published: 04 August 2023 Publication History

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).

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  • (2024)Editing Factual Knowledge and Explanatory Ability of Medical Large Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679673(2660-2670)Online publication date: 21-Oct-2024
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    cover image ACM Conferences
    KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2023
    5996 pages
    ISBN:9798400701030
    DOI:10.1145/3580305
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 04 August 2023

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    Author Tags

    1. online platform
    2. tag recommendation
    3. text classification

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    • Research-article

    Funding Sources

    • APRC - CityU New Research Initiatives
    • CCF-Tencent Open Fund
    • Ant Group (CCF-Ant Research Fund, Ant Group Research Fund)
    • SIRG - CityU Strategic Interdisciplinary Research Grant
    • CityU - HKIDS Early Career Research Grant
    • Huawei (Huawei Innovation Research Program)

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    View all
    • (2024)Editing Factual Knowledge and Explanatory Ability of Medical Large Language ModelsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679673(2660-2670)Online publication date: 21-Oct-2024
    • (2024)Bilateral Multi-Behavior Modeling for Reciprocal Recommendation in Online RecruitmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339770536:11(5681-5694)Online publication date: Nov-2024
    • (2024)CSIA-GCN: A Doctor Recommendation Model Based on Interactive Graph Convolutional Networks2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650337(1-8)Online publication date: 30-Jun-2024
    • (2023)Towards Automatic ICD Coding via Knowledge Enhanced Multi-Task LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615087(1238-1248)Online publication date: 21-Oct-2023
    • (2023)REST: Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615033(1278-1287)Online publication date: 21-Oct-2023

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