Lab Overview

CLEF eHealth will run in 2016:  https://sites.google.com/site/clefehealth2016/ 


*Check our proceedings!*


Problem: Laypeople find eHealth documents to be difficult to understand and also clinicians have problems in understanding the jargon of other professional groups even though laws and policies emphasise the need to document care in a comprehensive manner and provide further information on health conditions to help their understanding. A simple example from a US discharge document is “AP: 72 yo f w/ ESRD on HD, CAD, HTN, asthma p/w significant hyperkalemia & associated arrythmias.” However, authors of both care documents and consumer leaflets are overloaded with information and face many challenges in the timely and efficient generation, processing and sharing of such information. One example here is clinical handover between nurses, where verbal handover and note taking can lead to loss of information.


Usage scenario is to ease patients and nurses ease in understanding and accessing eHealth information. eHealth documents are much easier to understand after expanding shorthand, correcting the misspellings, normalising all health conditions to standardised terminology, and linking the words to a patient-centric search on the Internet. This would result in “Description of the patient's active problem: 72 year old female with dependence on hemodialysis, coronary heart disease, hypertensive disease, and asthma who is currently presenting with the problem of significant hyperkalemia and associated arrhythmias” with the highlighted words linked to their definitions in Consumer Health Vocabulary and other patient-friendly sources. In addition, auto converting a verbal nursing handover to text and then highlighting important information within the transcription for the next nurse would aid care documentation and release nurses time to, for example, discuss these resources and provide further information for a longer time with the patients.

This year, CLEF eHealth organizes 2 tasks: 

Task 1. Information extraction from Clinical Text

    Task 1a: Clinical speech recognition related to converting verbal nursing handover to written free-text records

    Task 1b: Named entity recognition in clinical reports 

Task 2: User-Centred Health Information Retrieval