Facilitating Access to Health Web Pages with Different Language Complexity Levels

Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and E-Health

et al. 2020

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A patient, nowadays, acquires health information from the Web mainly through a "human-to-machine" communication process with a generic search engine. This, in turn, affects, positively or negatively, his/her empowerment level and the "human-to-human" communication process that occurs between a patient and a healthcare professional such as a doctor. A generic communication process can be modelled by considering its syntactic-technical, semantic-meaning, and pragmatic-effectiveness levels and an efficacious communication occurs when all the communication levels are fully addressed. In the case of retrieval of health information from the Web, although a generic search engine is able to work at the syntactic-technical level, the semantic and pragmatic aspects are left to the user and this can be challenging, especially for elderly people. This work presents a custom search engine, FACILE, that works at the three communication levels and allows to overcome the challenges confronted during the search process. A patient can specify his/her information requirements in a simple way and FACILE will retrieve the "right" amount of Web content in a language that he/she can easily understand. This facilitates the comprehension of the found information and positively affects the empowerment process and communication with healthcare professionals. 1 INTRODUCTION According to the World Health Organization (WHO), empowerment is "a process through which people gain greater control over decisions and actions affecting their health" (WHO, 1998). It includes, as a basic step, the acquisition of health/medical information that helps patients/citizens to understand medical conditions and treatments, acquire selfconfidence to discuss them with medical professionals and, together, make the best-informed decisions (Akerkar & Bichile, 2004; Smith, 2004). The main source of health/medical information is, nowadays, the World Wide Web (or Web, for short) with the number of Web health information seekers that have been steadily increasing over the years (Pew Research Center, 2013; Taylor, 2010). Search engines are more and more used as the main tools to provide Web information. However, generic search engines do not make any distinction among the users and overload them with the amount of information. The use of a search engine for Web information retrieval entails a human-to-machine communication process between the user (e.g., a patient) and the search engine. It affects, among others, the amount, comprehension and use of the found information. This, in turn, may facilitate or complicate the humanto-human communication process between patients and healthcare professionals such as doctors (Smith, 2004). Communication processes have been modelled in various ways in the past decades. One of the most famous communication model is the one introduced by Morris in relation to its theory of signs (Morris, 1938). It is made up of three levels, i.e., syntactic, semantic and pragmatic. It is paired up by another communication model introduced by Shannon...

Section: Facile Implementationmentioning

confidence: 99%

Section: Experimental Usementioning

confidence: 99%

Tailored Retrieval of Health Information from the Web for Facilitating Communication and Empowerment of Elderly People

Proceedings of the 6th International Conference on Information and Communication Technologies for Ageing Well and E-Health

et al. 2020

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“…Some of the principles presented in this paper are based on the ones discussed in a previous work [6]. The present work, however, extends the previous study by including a literature survey on the health seekers requirements.…”

Section: Introductionmentioning

confidence: 77%

“…In particular, for the language complexity, the MedicalAudience 5 type plays a key role to identify searching mechanisms that provide targeted information. This type describes the target audiences for medical Web pages and it includes Patient 6 , Clinician 7 and MedicalResearcher 8 as more specific types. As reported in schema.org, a patient is any person recipient of health care services.…”

Section: Structured Data In Health Science Domain On the Webmentioning

confidence: 99%

Language Complexity in On-line Health Information Retrieval

Communications in Computer and Information Science

et al. 2020

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The number of people searching for on-line health information has been steadily growing over the years so it is crucial to understand their specific requirements in order to help them finding easily and quickly the specific information they are looking for. Although generic search engines are typically used by health information seekers as the starting point for searching information, they have been shown to be limited and unsatisfactory because they make generic searches, often overloading the user with the provided amount of results. Moreover, they are not able to provide specific information to different types of users. At the same time, specific search engines mostly work on medical literature and provide extracts from medical journals that are mainly useful for medical researchers and experts but not for non-experts. A question then arises: Is it possible to facilitate the search of on-line health/medical information based on specific user requirements? In this paper, after analysing the main characteristics and requirements of on-line health seeking, we provide a first answer to this question by exploiting the Web structured data for the health domain and presenting a system that allows different types of users, i.e., non-medical experts and medical experts, to retrieve Web pages with language complexity levels suitable to their expertise. Furthermore, we apply our methodology to the results of a generic search engine, such as Google, in order to re-rank them and provide different users with the proper health/medical Web pages in terms of language complexity.

“…The results shown in Table 3 allow ULearn to support a learning process in two different categories focusing more on the medical field. Although, more experiments are needed, we can preliminary assume to use the four categories for nonmedical experts, who have broader needs in terms of learning, and the two categories for medical experts, who have more specific/narrow needs in terms of learning, as discussed in [3], [15]. In this case, the balanced learning occurs when the experts mainly progress through the "deepening" and "widening" learning dimensions.…”

Section: Ulearn Implementation and Experimental Resultsmentioning

confidence: 99%

ULearn: Personalized Medical Learning on the Web for Patient Empowerment

Lecture Notes in Computer Science

et al. 2019

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Health literacy constitutes an important step towards patient empowerment and the Web is presently the biggest repository of medical information and, thus, the biggest medical resource to be used in the learning process. However, at present web medical information is mainly accessed through generic search engines that do not take into account the user specific needs and starting knowledge and so are not able to support learning activities tailored to the specific user requirements. This work presents "ULearn" a meta engine that supports access, understanding and learning on the Web in the medical domain based on specific user requirements and knowledge levels towards what we call "balanced learning". Balanced learning allows users to perform learning activities based on specific user requirements (understanding, deepinning, widening and exploring) towards his/her empowerment. We have designed and developed ULearn to suggest search keywords correlated to the different user requirements and we have carried out some preliminary experiments to evaluate the effectiveness of the provided information.