An Architecture for Diversity-aware Search for Medical Web Content

2012 ◽  
Vol 51 (06) ◽  
pp. 549-556 ◽  
Author(s):  
K. Denecke
Keyword(s):  
System Architecture ◽  
User Satisfaction ◽  
Medical Information ◽  
Medical Terminology ◽  
Web Content ◽  
Diversity Measures ◽  
Health Related ◽  
Retrieval Engine ◽  
Media Data ◽  
The Web

SummaryObjectives: The Web provides a huge source of information, also on medical and health-related issues. In particular the content of medical social media data can be diverse due to the background of an author, the source or the topic. Diversity in this context means that a document covers different aspects of a topic or a topic is described in different ways. In this paper, we introduce an approach that allows to consider the diverse aspects of a search query when providing retrieval results to a user.Methods: We introduce a system architecture for a diversity-aware search engine that allows retrieving medical information from the web. The diversity of retrieval results is assessed by calculating diversity measures that rely upon semantic information derived from a mapping to concepts of a medical terminology. Considering these measures, the result set is diversified by ranking more diverse texts higher.Results: The methods and system architecture are implemented in a retrieval engine for medical web content. The diversity measures reflect the diversity of aspects considered in a text and its type of information content. They are used for result presentation, filtering and ranking. In a user evaluation we assess the user satisfaction with an ordering of retrieval results that considers the diversity measures.Conclusions: It is shown through the evaluation that diversity-aware retrieval considering diversity measures in ranking could increase the user satisfaction with retrieval results.

Ontology-based approach to enhance medical web information extraction

2019 ◽  
Vol 15 (3) ◽  
pp. 359-382 ◽  
Author(s):  
Nassim Abdeldjallal Otmani ◽  
Malik Si-Mohammed ◽  
Catherine Comparot ◽  
Pierre-Jean Charrel
Keyword(s):  
Information Extraction ◽  
Medical Information ◽  
Real Life ◽  
Snomed Ct ◽  
Content Type ◽  
Meta Model ◽  
Gold Standard Corpus ◽  
Health Related ◽  
Medical Concepts ◽  
The Web

Purpose The purpose of this study is to propose a framework for extracting medical information from the Web using domain ontologies. Patient–Doctor conversations have become prevalent on the Web. For instance, solutions like HealthTap or AskTheDoctors allow patients to ask doctors health-related questions. However, most online health-care consumers still struggle to express their questions efficiently due mainly to the expert/layman language and knowledge discrepancy. Extracting information from these layman descriptions, which typically lack expert terminology, is challenging. This hinders the efficiency of the underlying applications such as information retrieval. Herein, an ontology-driven approach is proposed, which aims at extracting information from such sparse descriptions using a meta-model. Design/methodology/approach A meta-model is designed to bridge the gap between the vocabulary of the medical experts and the consumers of the health services. The meta-model is mapped with SNOMED-CT to access the comprehensive medical vocabulary, as well as with WordNet to improve the coverage of layman terms during information extraction. To assess the potential of the approach, an information extraction prototype based on syntactical patterns is implemented. Findings The evaluation of the approach on the gold standard corpus defined in Task1 of ShARe CLEF 2013 showed promising results, an F-score of 0.79 for recognizing medical concepts in real-life medical documents. Originality/value The originality of the proposed approach lies in the way information is extracted. The context defined through a meta-model proved to be efficient for the task of information extraction, especially from layman descriptions.

Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources

2011 ◽  
Vol 17 (2) ◽  
pp. 95-115 ◽  
Author(s):  
Miguel A. Mayer ◽  
Pythagoras Karampiperis ◽  
Antonis Kukurikos ◽  
Vangelis Karkaletsis ◽  
Kostas Stamatakis ◽  
...  
Keyword(s):  
Semantic Web ◽  
Search Engines ◽  
Medical Information ◽  
Main Tool ◽  
Web Content ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Web Resources ◽  
Health Related ◽  
Machine Readable

The number of health-related websites is increasing day-by-day; however, their quality is variable and difficult to assess. Various “trust marks” and filtering portals have been created in order to assist consumers in retrieving quality medical information. Consumers are using search engines as the main tool to get health information; however, the major problem is that the meaning of the web content is not machine-readable in the sense that computers cannot understand words and sentences as humans can. In addition, trust marks are invisible to search engines, thus limiting their usefulness in practice. During the last five years there have been different attempts to use Semantic Web tools to label health-related web resources to help internet users identify trustworthy resources. This paper discusses how Semantic Web technologies can be applied in practice to generate machine-readable labels and display their content, as well as to empower end-users by providing them with the infrastructure for expressing and sharing their opinions on the quality of health-related web resources.

Semantics and the Medical Web

2011 ◽  
pp. 266-278
Author(s):  
Amanda Spink ◽  
Robert M. Wolfe ◽  
Bernard J. Jansen
Keyword(s):  
Longitudinal Study ◽  
Health Information ◽  
Search Engine ◽  
Web Search ◽  
Web Searching ◽  
Medical Terminology ◽  
Web Search Engine ◽  
Health Related ◽  
Medical Health ◽  
The Web

This chapter discusses issues related to semantics and the medical Web. Much health information is available on the Web, but not always effectively found by users. Studies examining various aspects of medical Web searching show that searchers’ do not always use correct medical terminology. This chapter reports results from a longitudinal study of medical/health related searches using commercial Web search engine query data from 1997 to 2004.

Digging for the truth: the case for active annotation in evaluating the credibility of online medical information (Preprint)

2020 ◽  
Author(s):  
Mikołaj Morzy ◽  
Bartłomiej Balcerzak ◽  
Adam Wierzbicki ◽  
Adam Wierzbicki
Keyword(s):  
Machine Learning ◽  
Medical Information ◽  
Representation Learning ◽  
Training Dataset ◽  
Highly Qualified ◽  
Human In The Loop ◽  
Annotation Process ◽  
Comprehensive Framework ◽  
Online Sources ◽  
The Web

BACKGROUND With the rapidly accelerating spread of dissemination of false medical information on the Web, the task of establishing the credibility of online sources of medical information becomes a pressing necessity. The sheer number of websites offering questionable medical information presented as reliable and actionable suggestions with possibly harmful effects poses an additional requirement for potential solutions, as they have to scale to the size of the problem. Machine learning is one such solution which, when properly deployed, can be an effective tool in fighting medical disinformation on the Web. OBJECTIVE We present a comprehensive framework for designing and curating of machine learning training datasets for online medical information credibility assessment. We show how the annotation process should be constructed and what pitfalls should be avoided. Our main objective is to provide researchers from medical and computer science communities with guidelines on how to construct datasets for machine learning models for various areas of medical information wars. METHODS The key component of our approach is the active annotation process. We begin by outlining the annotation protocol for the curation of high-quality training dataset, which then can be augmented and rapidly extended by employing the human-in-the-loop paradigm to machine learning training. To circumvent the cold start problem of insufficient gold standard annotations, we propose a pre-processing pipeline consisting of representation learning, clustering, and re-ranking of sentences for the acceleration of the training process and the optimization of human resources involved in the annotation. RESULTS We collect over 10 000 annotations of sentences related to selected subjects (psychiatry, cholesterol, autism, antibiotics, vaccines, steroids, birth methods, food allergy testing) for less than $7 000 employing 9 highly qualified annotators (certified medical professionals) and we release this dataset to the general public. We develop an active annotation framework for more efficient annotation of non-credible medical statements. The results of the qualitative analysis support our claims of the efficacy of the presented method. CONCLUSIONS A set of very diverse incentives is driving the widespread dissemination of medical disinformation on the Web. An effective strategy of countering this spread is to use machine learning for automatically establishing the credibility of online medical information. This, however, requires a thoughtful design of the training pipeline. In this paper we present a comprehensive framework of active annotation. In addition, we publish a large curated dataset of medical statements labelled as credible, non-credible, or neutral.

scholarly journals A MODEL BASED ON FUZZY LINGUISTIC INFORMATION TO EVALUATE THE QUALITY OF DIGITAL LIBRARIES

2010 ◽  
Vol 09 (03) ◽  
pp. 455-472 ◽  
Author(s):  
F. J. CABRERIZO ◽  
J. LÓPEZ-GIJÓN ◽  
A. A. RUÍZ ◽  
E. HERRERA-VIEDMA
Keyword(s):  
Digital Libraries ◽  
User Satisfaction ◽  
Information Access ◽  
Great Influence ◽  
Linguistic Information ◽  
Model Based ◽  
Global Quality ◽  
Fuzzy Linguistic ◽  
The Web

The Web is changing the information access processes and it is one of the most important information media. Thus, the developments on the Web are having a great influence over the developments on others information access instruments as digital libraries. As the development of digital libraries is to satisfy user need, user satisfaction is essential for the success of a digital library. The aim of this paper is to present a model based on fuzzy linguistic information to evaluate the quality of digital libraries. The quality evaluation of digital libraries is defined using users' perceptions on the quality of digital services provided through their Websites. We assume a fuzzy linguistic modeling to represent the users' perception and apply automatic tools of fuzzy computing with words based on the LOWA and LWA operators to compute global quality evaluations of digital libraries. Additionally, we show an example of application of this model where three Spanish academic digital libraries are evaluated by fifty users.

A QoS-aware approach for runtime discovery, selection and composition of semantic web services

2016 ◽  
Vol 12 (2) ◽  
pp. 177-200 ◽  
Author(s):  
Sanjay Garg ◽  
Kirit Modi ◽  
Sanjay Chaudhary
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Web Service ◽  
User Satisfaction ◽  
Service Discovery ◽  
Semantic Web Services ◽  
Functional Requirements ◽  
Semantic Description ◽  
Content Type ◽  
The Web

Purpose Web services play vital role in the development of emerging technologies such as Cloud computing and Internet of Things. Although, there is a close relationship among the discovery, selection and composition tasks of Web services, research community has treated these challenges at individual level rather to focus on them collectively for developing efficient solution, which is the purpose of this work. This paper aims to propose an approach to integrate the service discovery, selection and composition of Semantic Web services on runtime basis. Design/methodology/approach The proposed approach defined as a quality of service (QoS)-aware approach is based on QoS model to perform discovery, selection and composition tasks at runtime to enhance the user satisfaction and quality guarantee by incorporating non-functional parameters such as response time and throughput with the Web services and user request. In this paper, the proposed approach is based on ontology for semantic description of Web services, which provides interoperability and automation in the Web services tasks. Findings This work proposed an integrated framework of Web service discovery, selection and composition which supports end user to search, select and compose the Web services at runtime using semantic description and non-functional requirements. The proposed approach is evaluated by various data sets from the Web Service Challenge 2009 (WSC-2009) to show the efficiency of this work. A use case scenario of Healthcare Information System is implemented using proposed work to demonstrate the usability and requirement the proposed approach. Originality/value The main contribution of this paper is to develop an integrated approach of Semantic Web services discovery, selection and composition by using the non-functional requirements.

Measuring user satisfaction on the web

2003 ◽  
pp. 293-297
Author(s):  
Cathy Dudek ◽  
Gitte Lindgaard
Keyword(s):  
User Satisfaction ◽  
The Web

scholarly journals Mining Social Media Data for Biomedical Signals and Health-Related Behavior

2020 ◽  
Vol 3 (1) ◽  
pp. 433-458 ◽  
Author(s):  
Rion Brattig Correia ◽  
Ian B. Wood ◽  
Johan Bollen ◽  
Luis M. Rocha
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Population Level ◽  
Data Access ◽  
Health Conditions ◽  
Social Phenomena ◽  
Medical Treatments ◽  
Social Media Data ◽  
Health Related ◽  
Media Data

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

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