- Task 1: Multilingual Information Extraction – ICD10 coding
- Task 2: Technology Assisted Reviews in Empirical Medicine
- Task 3: Consumer Health Search
CLEF eHealth 2018 Programme, September 2018
14:30 – 16:00 Labs eHealth (1) – Lab overview broken down as:
14:30 – 14:40 Welcome and Introduction
14:40 – 15:30 Keynote (40minutes + 10 minutes questions) – Prof. Sophia Ananiadou [details below]
15:30 – 16:00 Task 1, Task 2, Task 3 Overview (10 minutes each)
16:00 – 16:30 Tea & Coffee Break
16:30 – 18:00 Labs eHealth (2) – Task 1 IE Session
13:30-14:30 Poster Session (all who submitted working notes to CLEF eHealth 2018 are invited to present a poster describing their contribution to the lab)
14:30 – 16:00 Labs e-Health (3) – Task 2 TAR Session
16:00 – 16:30 Tea & Coffee Break
16:30 – 18:00 Labs e-Health (4) – Task 3 IR Session
*See the CLEF website for full conference schedule: http://clef2018.clef-initiative.eu/index.php?page=Pages/programme.html
Keynote details [Prof. Sophia Ananiadou – Monday 10th Sept. @ 14:40]:
Title: The Big Mechanism: extracting cancer mechanisms from the literature
Abstract: One of the aims of the Big Mechanism is using text mining to link cancer pathway models with textual evidence. In this way we can automate science for drug discovery in cancer research. Text mining techniques are being employed to construct, update and verify information in relevant models, to ensure that the information used for hypothesis generation is as accurate as possible. Complex information from the literature (semantic events) are automatically extracted and mapped/compared to reactions in existing pathway models.
These comparisons allow the existing models to be verified or updated in several ways. Information from the literature can act as corroborative evidence of the validity of these reactions in a model or help to extend it. In addition, by taking into account textual context (uncertainty, negation), we can provide a confidence measure for linking and ranking evidence from the literature for model curation and experimental design.
Sophia Ananiadou is Professor in Computer Science at the School of Computer Science, Director of the National Centre for Text Mining and Alan Turing Fellow. She played a leading role in bridging text mining to systems biology, systems medicine, public health and clinical text mining. Recent research includes text mining methods for the curation of pathways, the development of systems to support Public Health systematic reviews, and interoperable text mining infrastructure. Her research has featured in the Lancet, the Guardian, New Scientist, Nature, Pharma Technology Focus, Huffington Post, etc.
CLEF eHealth 2018:
In today’s information overloaded society it is increasingly difficult to retrieve and digest valid and relevant information to make health-centered decisions. Medical content is becoming available electronically in a variety of forms ranging from patient records and medical dossiers, scientific publications and health-related websites to medical-related topics shared across social networks. Laypeople, clinicians and policy-makers need to easily retrieve, and make sense of medical content to support their decision making. Information retrieval systems have been commonly used as a means to access health information available online. However, the reliability, quality, and suitability of the information for the target audience varies greatly while high recall or coverage, that is finding all relevant information about a topic, is often as important as high precision, if not more. Furthermore, the information seekers in the health domain also experience difficulties in expressing their information needs as search queries.
CLEF eHealth aims to bring together researchers working on related information access topics and provide them with datasets to work with and validate the outcomes. This, the sixth year of the lab, offers the following three tasks.
Task 1. Multilingual Information Extraction
Task 2. Technologically Assisted Reviews in Empirical Medicine
Task 3. Patient-centred information retrieval
The lab also offers a student mentoring track. Contact the lab chairs if you are interested in taking part in this track.
The vision for the Lab is two-fold: (1) to develop tasks that potentially impact patient understanding of medical information and (2) to provide the community with an increasingly sophisticated dataset of clinical narrative, enriched with links to evidence-based care guidelines, systematic reviews, and other further information, to advance the state-of-the-art in multilingual information extraction and information retrieval in health care. Furthermore, we aim to support reproducible research by encouraging participants to reflect on methods and practical steps to take to facilitate the replication of their experiments. In particular, we call participants to submit their systems and configuration files, and independent researchers to reproduce the results of the participating teams.