Effective Medical Archives Processing Using Knowledge Graphs

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2019-07-18 DOI:10.1145/3331184.3331350

Xiaoli Wang, Rongzheng Wang, Z. Bao, Jiaying Liang, Wei Lu

{"title":"Effective Medical Archives Processing Using Knowledge Graphs","authors":"Xiaoli Wang, Rongzheng Wang, Z. Bao, Jiaying Liang, Wei Lu","doi":"10.1145/3331184.3331350","DOIUrl":null,"url":null,"abstract":"Medical archives processing is a very important task in a medical information system. It generally consists of three steps: medical archives recognition, feature extraction and text classification. In this paper, we focus on empowering the medical archives processing with knowledge graphs. We first build a semantic-rich medical knowledge graph. Then, we recognize texts from medical archives using several popular optical character recognition (OCR) engines, and extract keywords from texts using a knowledge graph based feature extraction algorithm. Third, we define a semantic measure based on knowledge graph to evaluate the similarity between medical texts, and perform the text classification task. This measure can value semantic relatedness between medical documents, to enhance the text classification. We use medical archives collected from real hospitals for validation. The results show that our algorithms can significantly outperform typical baselines that employs only term statistics.","PeriodicalId":20700,"journal":{"name":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3331184.3331350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 7

Abstract

Medical archives processing is a very important task in a medical information system. It generally consists of three steps: medical archives recognition, feature extraction and text classification. In this paper, we focus on empowering the medical archives processing with knowledge graphs. We first build a semantic-rich medical knowledge graph. Then, we recognize texts from medical archives using several popular optical character recognition (OCR) engines, and extract keywords from texts using a knowledge graph based feature extraction algorithm. Third, we define a semantic measure based on knowledge graph to evaluate the similarity between medical texts, and perform the text classification task. This measure can value semantic relatedness between medical documents, to enhance the text classification. We use medical archives collected from real hospitals for validation. The results show that our algorithms can significantly outperform typical baselines that employs only term statistics.

查看原文本刊更多论文

利用知识图谱有效地处理医疗档案

医疗档案处理是医疗信息系统中的一项重要工作。它一般包括三个步骤:医学档案识别、特征提取和文本分类。本文主要研究如何利用知识图谱来增强医学档案处理的能力。我们首先构建一个语义丰富的医学知识图谱。然后，使用几种常用的光学字符识别(OCR)引擎对医学档案文本进行识别，并使用基于知识图的特征提取算法从文本中提取关键字。第三，我们定义了一个基于知识图的语义度量来评估医学文本之间的相似度，并执行文本分类任务。该度量可以衡量医学文献之间的语义相关性，以增强文本分类。我们使用真实医院的医疗档案进行验证。结果表明，我们的算法可以显著优于仅使用术语统计的典型基线。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

自引率

0.00%

发文量