Characterizing Health-Related Community Question Answering

Our ongoing project is aimed at improving information access to narrow-domain collections of questions and answers. This poster demonstrates how out-of-the-box tools and domain dictionaries can be applied to community question answering (CQA) content in health domain. This approach can be used to improve user interfaces and search over CQA data, as well as to evaluate content quality. The study is a first-time use of a sizable dataset from the Russian CQA site [email protected]

Towards Automatic Evaluation of Health-Related CQA Data

The paper reports on evaluation of Russian community question answering (CQA) data in health domain. About 1,500 question-answer pairs were manually evaluated by medical professionals, in addition automatic evaluation based on reference disease-medicine pairs was performed. Although the results of the manual and automatic evaluation do not fully match, we find the method still promising and propose several improvements. Automatic processing can be used to dynamically monitor the quality of the CQA content and to compare different data sources. Moreover, the approach can be useful for symptomatic surveillance and health education campaigns.This work is partially supported by the Russian Foundation for Basic Research, project #14-07-00589 “Data Analysis and User Modelling in Narrow-Domain Social Media”. We also thank assessors who volunteered for the evaluation and Mail.Ru for granting us access to the data

Specialised tools are needed when searching the web for rare disease diagnoses

In our recent paper, we study web search as an aid in the process of diagnosing rare diseases. To answer the question of how well Google Search and PubMed perform, we created an evaluation framework with 56 diagnostic cases and made our own specialized search engine, FindZebra (findzebra.com). FindZebra uses a set of publicly available curated sources on rare diseases and an open-source information retrieval system, Indri. Our evaluation and the feedback received after the publication of our paper both show that FindZebra outperforms Google Search and PubMed. In this paper, we summarize the original findings and the response to FindZebra, discuss why Google Search is not designed for specialized tasks and outline some of the current trends in using web resources and social media for medical diagnosis

Anxiety sensitivity, uncertainty and recursive thinking: A continuum on Cyberchondria conditions during the Covid Outbreak

Background. Cyberchondria is a term used to refer to excessive surfing the web looking for health care information, excessive checking behavior being related to health-related anxiety. This period of quarantine for the Covid-19 pandemic is increasing the pathological use of the internet, and the excessive surfing the web looking for health care information. Another dimension related to the Covid-19 outbreak refers to uncertainty intolerance, for this reason being necessary for the healthcare professionals to provide clear and linear information. Aim. The aim of this review is to identify the psychological correlations connected to cyberchondria in the quarantine period. Methods. Following the PRISMA guidelines, we carried out a systematic review of the literature on PubMed. The terms used for the search were “Cyberchondria” OR “Anxiety” AND “Quarantine”. Results. As resulting from the reviewed literature, there is a relationship between anxiety for one’s own state of health and cyberchondria, with negative psychological effects of quarantine, including post-traumatic stress symptoms, depression, anxiety, low mood, irritability, insomnia, uncertainty, emotional exhaustion, this condition being associated with hypervigilance, and catastrophic misinterpretation of bodily signs. Conclusion. In the light of this and according to the literature, it would be desirable that research can further explore the factors influencing the increase in cyberchondria in the future

INNOVATION IN DESIGNING HEALTH INFORMATION WEBSITES: RESULTS FROM A QUANTITATIVE STUDY

A wealth of health information exists on the Internet, but successfully finding that information is not easy. One of the issues causing this is the lack of tools for exploring information and assisting in navigation within health websites. As a result, relevant information cannot be easily discovered. We hope to rectify this issue from the design perspective. Based on previous work, we have created a prototype website called Better Health Explorer to better support such information seeking behaviours. This paper reports on a quantitative study evaluating this prototype. The results demonstrate several improvements in health information seeking supported by the tool. Furthermore, we have identified three general design characteristics that should to be considered when designing consumer health websites. These findings have design implications for health information seeking applications, as well as identifying directions for further research

ONLINE HEALTH INFORMATION SEEKING BEHAVIOUR: UNDERSTANDING DIFFERENT SEARCH APPROACHES

People intuitively use search engines to look for health information. However, people take an exploratory search approach to find the information in some scenarios, and current search engines do not support these cases well. This exploratory information seeking behaviour is rarely investigated by researchers in the context of online consumer health information. We report on a qualitative study to conceptualise the health information seeking behaviour of lay-people. This paper describes the result of this study, and makes a contribution towards a conceptual understanding of search approaches by people seeking health information, search strategies used by health information seekers, and design implications for providing a better exploratory health search experience

Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs

Search engine logs have a great potential in tracking and predicting outbreaks of infectious disease. More precisely, one can use the search volume of some search terms to predict the infection rate of an infectious disease in nearly real-time. However, conducting accurate and stable prediction of outbreaks using search engine logs is a challenging task due to the following two-way instability characteristics of the search logs. First, the search volume of a search term may change irregularly in the short-term, for example, due to environmental factors such as the amount of media or news. Second, the search volume may also change in the long-term due to the demographic change of the search engine. That is to say, if a model is trained with such search logs with ignoring such characteristic, the resulting prediction would contain serious mispredictions when these changes occur. In this work, we proposed a novel feature selection method to overcome this instability problem. In particular, we employ a seasonal-adjustment method that decomposes each time series into three components: seasonal, trend and irregular component and build prediction models for each component individually. We also carefully design a feature selection method to select proper search terms to predict each component. We conducted comprehensive experiments on ten different kinds of infectious diseases. The experimental results show that the proposed method outperforms all comparative methods in prediction accuracy for seven of ten diseases, in both now-casting and forecasting setting. Also, the proposed method is more successful in selecting search terms that are semantically related to target diseases.Comment: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '19