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Study on Rules of Medication in Breast Cancer Based on Literature Information Extraction

Published: 05 April 2024 Publication History

Abstract

The literature contains a large amount of diagnosis and treatment information, and manual information extraction will consume a lot of human resources. To solve this problem, information extraction technology is used to automatically extract diagnosis and treatment information from traditional Chinese medicine (TCM) literature, and then explore the medication patterns of diseases. This study takes breast cancer as an example to carry out practice. First, we collected 2104 articles related to breast cancer treated with TCM. Then select the most suitable entity recognition model from multiple information extraction models, and finally obtain the UIE model with an F1 score of 89.69%. The UIE model was used to extract 5 types of entities of Chinese medicine, prescription, syndrome, symptom and disease. Finally, conduct statistical, association, clustering, and other data analysis on the entity. There were 36 prescriptions and 232 flavors of Chinese medicine with high frequency. Most of the four qi, five flavors and channel entry were warm, sweet and liver channel. The core drug pair was ovate atractylodes - codonopsis. Finally, it is concluded that activating qi and blood, soothing liver and invigorating spleen, clearing heat and detoxifying are the basic therapeutic principles of TCM in the treatment of breast cancer, it mainly treats breast cancer by regulating the operation of qi and blood, regulating liver and spleen functions and removing the dampness and heat in the body. This study also explores a new path to automatically extract diagnosis and treatment knowledge from literature by using information extraction technology, and utilizes AI technology to help large-scale medical knowledge discovery.

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ISAIMS '23: Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
October 2023
1394 pages
ISBN:9798400708138
DOI:10.1145/3644116
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 April 2024

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