Making semantic annotation on patient data of depression

Yanan Du, Shaofu Lin, Zhisheng Huang

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

22 Downloads (Pure)


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 publicationICMHI '18
Subtitle of host publicationProceedings of the 2nd International Conference on Medical and Health Informatics (ICMHI 2018)
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450363891
Publication statusPublished - Jun 2018
Event2nd International Conference on Medical and Health Informatics, ICMHI 2018 - Tsukuba, Japan
Duration: 8 Jun 201810 Jun 2018


Conference2nd International Conference on Medical and Health Informatics, ICMHI 2018


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


Dive into the research topics of 'Making semantic annotation on patient data of depression'. Together they form a unique fingerprint.

Cite this