Our research has developed advanced natural language processing, information retrieval, and machine learning approaches to overcome the problems of ‘understanding and reasoning with clinical data’.

Medical Free Text Retrieval & Analytics

The majority of health data is recorded in unstructured free-text; clinical examination reports, nursing notes, discharge summaries, death certificates are just some examples. This data contains information that is valuable for secondary use, such as for population health monitoring and reporting. However, its clinical importance and large volume hinders manual analysis of such data. As a consequence, the analysis of clinical data is often performed retrospectively with delays that potentially undermine effective population health monitoring and reporting.

Our research has developed advanced natural language processing, information retrieval, and machine learning approaches to overcome the problems of “understanding and reasoning with clinical data”. In additional, we emphasise the use of standard clinical terminologies grounded in description logic.

We have delivered effective automated health monitoring and reporting solutions, including:

  • the analysis of pathology reports and death certificates to timely assess the incidence of cancer and the associated mortality rates,
  • the analysis of radiology reports to support the reconciliation of radiology findings with emergency department discharge records,
  • the analysis of medical reports to provide capability for medical record searching and analytics,
  • the analysis of medical forums to identify adverse drug reactions

Software

Medtex, RadSearch, CADEminer

Our solutions have been developed in partnership with healthcare practitioners from Cancer Registries, hospital radiology and emergency medicine departments. Working with health industry stakeholders allows Medtex to provide informed decision support by extracting greater value from their clinical narrative reports. For more information, contact Dr. Anthony Nguyen.

Medtex

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Reading and processing narrative-based clinical reports is an extremely labour and time-consuming process. To ease the workload of clinical staff and aid the computer processing of these reports, Medtex, a smart clinical natural language processing software, has been developed. The software extracts meaningful information from free text data to aid decision support and take the weight off clinical staff.

ChallengeSolutionScreenshotPublications
An extensive amount of clinical data is still stored as unstructured free-text and the information is often trapped within the language used in these reports. The reports are in the form of unstructured, ungrammatical, and often fragmented free-text.

Clinical information abstraction from patients’ clinical data relies on manual inspections and experience-based judgements from clinical staff. The effort required for information abstraction is extremely labour and time intensive, prone to human errors and ineffective.

A simple way of easily and consistently using free-text clinical data can improve both health outcomes for patients, boost the efficiency of the health system and provide a rich data set for further research.

Medtex, a semantic medical text analysis software, is a tool for informing clinical decision making by analysing free-text clinical documents.

Medtex works by “learning” what statements to look for, and uses SNOMED CT, the internationally defined set of clinical terms, to unify and reason with the language across information sources. It incorporates domain knowledge to bridge the gap between natural language and the use of clinical terminology semantics for automatic medical text inference and reasoning.

Analysis engines using the Medtex technology [1] have been developed to:

  • standardise the free text by identifying medical concepts, abbreviations and acronyms, shorthand terms, dimensions and relevant legacy codes;
  • relate key medical concepts, terms and codes using contextual information and report substructure; and
  • use formal semantics to reason with the clinical concepts; inferring complex clinical notions relevant to a health application.

Medtex scales to large amounts of unstructured data and have been integrated within a highly distributed computational framework. It turns the medical narrative into structured data that can be easily stored, queried or rendered by most systems for use in their health application.

Medtex has been used to deliver the following solutions to healthcare practitioners from Cancer Registries, and hospital radiology and emergency medicine departments:

  • the analysis of pathology reports and death certificates to timely assess the incidence of cancer and the associated mortality rates,
  • the analysis of radiology reports to support the reconciliation of radiology findings with emergency department discharge records,
  • the analysis of medical reports to provide capability for medical record searching and analytics

For more information, contact Dr. Anthony Nguyen.

Medtex electronic health record flow chart diagram
  1. Nguyen A, Lawley M, Hansen D, Colquist S. A simple pipeline application for identifying and negating SNOMED clinical terminology in free text. Health Informatics Conference, 2009;188-193.