Making semantic annotation on patient data of depression

Yanan Du, Shaofu Lin, Zhisheng Huang

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Patient data, more exactly, electronic medical records (EMR), usually contain a lot of free texts. Those unstructured medical data cannot be easily understood by computers. In addition, EMR data have a strong privacy, which hinders the sharing and use of medical data and makes it impossible to conduct more in-depth medical research. This paper presents a method of the realization of semantic EMR by making semantic annotations on free texts in medical records. We will show how to use Natural Language Processing (NLP) tools to create semantic annotation with wellknown biomedical terminologies/ontologies such as the Unified Medical Language System (UMLS). Moreover, we will describe how to make the semantic annotations on a set of virtual patient data for depression, which are generated by using the Advanced Patient Data Generator (APDG), a knowledge-based patient data generator. In short, our goal is to use semantic technology to improve the sharing and utilization of medical data and the interoperability among systems.

Original languageEnglish
Title of host publicationProceedings of 2018 the 2nd International Conference on Medical and Health Informatics (ICMHI 2018)
PublisherAssociation for Computing Machinery
Pages134-137
Number of pages4
ISBN (Electronic)9781450363891
DOIs
Publication statusPublished - 2018
Event2nd International Conference on Medical and Health Informatics, ICMHI 2018 - Tsukuba, Japan
Duration: 8 Jun 201810 Jun 2018

Conference

Conference2nd International Conference on Medical and Health Informatics, ICMHI 2018
CountryJapan
CityTsukuba
Period8/06/1810/06/18

Keywords

  • Data Integration
  • Depression
  • Electronic Medical Record
  • Semantic Annotation
  • Semantic Technology

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