QUT ielab at CLEF 2017 e-Health IR Task: Knowledge Base Retrieval for Consumer Health Search

In this paper we describe our participation to the CLEF 2017 e-Health IR Task [6]. This track aims to evaluate and advance search technologies aimed at supporting consumers to and health advice online. Our solution addressed this challenge by developing a knowledge base (KB) query expansion method. We found that the two best KB query expansion methods are mapping entity mentions to KB entities by performing exact matching entity mentions to the KB aliases (EM-Aliases) and multi-matching entity mentions to all KB features (Title, Categories, Links, Aliases, and Body) (EM-All). After mapping between entity mentions to KB entities established, we found the Title of the mapped KB entities as the best source of expansion terms compared to the aliases or combination of both features. Finally, we also found that Relevance Feedback and Pseudo Relevance Feedback are effective to further improve the query effectiveness

LIMSI@ CLEF eHealth 2015-task 2.

International audienceThis paper presents LIMSI’s participation in the User-Centered Health Information Retrieval task (task 2) at the CLEF eHealth 2015 workshop. In our contribution we explored two different strategies to query expansion, i.e. one based on entity recognition using MetaMap and the UMLS, and a second strategy based on disease hypothesis generation using self-constructed external resources such a corpus of Wikipedia pages describing diseases and conditions, and web pages from the Medline Plus health portal. Our best-scoring run was a weighed UMLS-based run which put emphasis on incorporating signs and symptoms recognized in the topic text by MetaMap. This run achieved a P@10 score of 0.262 and nDCG@10 of 0.196, respectively

Stigma related to Asking for Help from a Mental Health Professional in Bandung, Indonesia

Sejumlah remaja di Bandung, mengeluh memiliki ciri-ciri gangguan kesehatan mental tetapi, mereka tidak ingin mengungkapkan situasi tersebut kepada profesional kesehatan mental karena takut stigma masyarakat. Penelitian ini adalah deskriptif kualitatif menggunakan metode wawancara dengan rumusan masalah sebagai berikut (1) Apakah seorang individu akan mencari pertolongan dari profesional kesehatan mental jika merasa memiliki gangguan kesehatan mental. (2) Apa stigma yang dikatakan jika mereka mencari bantuan profesional kesehatan mental. Subjek penelitian ini terdiri dari 25 informan , usia 18 hingga 24 tahun. Ada 25 informan yang mengatakan bahwa mereka ada keinginan untuk mencari profesional kesehatan mental tapi mereka takut melakukannya karena stigma

Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information

An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs

PUBLIC STIGMA TOWARDS THE PRESENTATION OF SELF-DIAGNOSED MENTAL ILLNESS

Negative attitudes toward people with mental illnesses leads these individuals to utilize non-traditional avenues of support-seeking, including online venues. Within these venues, particularly the website Tumblr, the practice of self-diagnosing is common. At present, self-diagnosing is understudied, making it difficult to determine if self-diagnosed individuals face public stigma. Thus, one question about this phenomenon is as follows: are people who present themselves online as self-diagnosed stigmatized compared to those who present themselves as professionally diagnosed? The proposed thesis will address this question. Participants will view one of three Tumblr blogs (professionally diagnosed, self-diagnosed, and no diagnosis). It is hypothesized that participants will express differential desires to distance themselves from individuals who claim to have been self-diagnosed as compared to individuals who say they have been professionally diagnosed

Payoffs and pitfalls in using knowledge‑bases for consumer health search

Consumer health search (CHS) is a challenging domain with vocabulary mismatch and considerable domain expertise hampering peoples’ ability to formulate effective queries. We posit that using knowledge bases for query reformulation may help alleviate this problem. How to exploit knowledge bases for effective CHS is nontrivial, involving a swathe of key choices and design decisions (many of which are not explored in the literature). Here we rigorously empirically evaluate the impact these different choices have on retrieval effectiveness. A state-of-the-art knowledge-base retrieval model—the Entity Query Feature Expansion model—was used to evaluate these choices, which include: which knowledge base to use (specialised vs. general purpose), how to construct the knowledge base, how to extract entities from queries and map them to entities in the knowledge base, what part of the knowledge base to use for query expansion, and if to augment the knowledge base search process with relevance feedback. While knowledge base retrieval has been proposed as a solution for CHS, this paper delves into the finer details of doing this effectively, highlighting both payoffs and pitfalls. It aims to provide some lessons to others in advancing the state-of-the-art in CHS