The Challenge

Search technologies are critical to enable clinical staff to rapidly and effectively access patient information contained in free-text medical records. Medical search is challenging as it suffers from the semantic gap problem: the mismatch between the raw data and the way a human being interprets it. Valuable domain knowledge explicitly represented in structured knowledge resources such as ontologies (e.g. SNOMED CT) can potentially be leveraged to support such semantic inferences.

Our Solution

The focus of our research is on medical record searching and analytics using text [1], concepts [2], annotations [3], and SNOMED CT subsumption and relation querying [4-6].

For more information, contact Dr. Anthony Nguyen.

Xray of a bone fracture to lower limb
[/su_tab]

  1. Koopman B, Nguyen A. RadSearch: A search and analysis engine for free-text radiology reports. Health Informatics Conference, 2015.
  2. Koopman B, Zuccon G, Bruza P, Sitbon L, Lawley M. An Evaluation of Corpus-driven Measures of Medical Concept Similarity for Information Retrieval. CIKM, pg. 2439-2442, 2012.
  3. Metke A. ASE: A Search Engine for Semantically Annotated Documents. SNOMED CT Implementation Showcase 2014
  4. Koopman B, Zuccon G, Nguyen A, Vickers D, Butt L, Bruza P. Exploiting SNOMED CT Concepts & Relationships for Clinical Information Retrieval: AEHRC and QUT at the TREC Medical Track. TREC, 2012.
  5. Zuccon G, Koopman B, Nguyen A, Vickers D, Butt L. Exploiting Medical Hierarchies for Concept-based Information Retrieval. ADCS, pg. 111-114, 2012.
  6. Koopman B. Semantic Search as Inference: Applications in Health Informatics. PhD thesis, Queensland University of Technology, Brisbane, Australia, 2014.