Crowdsourced healthcare knowledge creation using patients’ health experience-ontologies

Sohn, Mye M.; Jeong, Sunghwan; Kim, Jongmo; Lee, Hyun Jung

doi:10.1007/s00500-017-2529-3

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Sohn et al introduced the Crowdsourced Health Experience-ontologies-based healthcare Knowledge Creation (CHEKC) framework, which leverages experience-ontologies to integrate healthcare knowledge and provides patients with healthcare information based on similar healthcare experiences, symptoms, and conditions, demonstrating superior efficiency and accuracy over the PatienstLikeMe.com (PLM) framework through two experiments, underscoring the potential of social media in research and data collection [41].…”

Section: Social Media In Healthcare Researchmentioning

confidence: 99%

Multifaceted Role of Social Media in Healthcare: Opportunities, Challenges, and the Need for Quality Control

Jeyaraman,

Ramasubramanian,

Kumar

et al. 2023

Cureus

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Section: Social Media In Healthcare Researchmentioning

confidence: 99%

Multifaceted Role of Social Media in Healthcare: Opportunities, Challenges, and the Need for Quality Control

Jeyaraman,

Ramasubramanian,

Kumar

et al. 2023

Cureus

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“…To a lesser extent we found papers where ontologies supported data integration, for example, in the healthcare [43] and multimedia processing [7] domains (SW4HC-DataIntegr.). Automated reasoning on formally represented knowledge is harnessed (SW4HC-Reasoning) in order to optimize the collection of missing values with crowdsourcing [46] or to validate the quality of data collected through crowdsourcing [22].…”

Section: Semantic Web For Human Computationmentioning

confidence: 99%

Semantic Web and Human Computation: The status of an emerging field

Sabou

Aroyo

Bontcheva

et al. 2018

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This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did this research line evolve during the last decade? How do the papers in this special issue align with the main lines of work of the community? In this editorial we inspect and reflect on the evolution of research at the intersection of Semantic Web and Human Computation. We use a methodology based on Systematic Mapping Studies to collect quantitative bibliographic data which we analyze through the lens of research topics envisioned by the research manifesto to characterize the evolution of research in this area, thus providing a context for introducing the papers of this special issue. We found evidences of a thriving research field; while steadily maturing, the field offers a number of open research opportunities for work where Semantic Web best practices and techniques are applied to support and improve the state-of-the-art in Human Computation, but also for work that exploits the strength of both areas to address scientifically and societally relevant issues.

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“…SC addresses the derivation and matching of the semantics of computational content and that of naturally expressed user intentions and brings together those disciplines concerned with connecting the intentions of humans with computational content by retrieving, using and manipulating existing content according to user's goals, and by creating, rearranging and managing content that matches the author's intentions (cf., [42]). SC technologies have been classified into five classes, namely, Semantic Analysis, Semantic Integration, Semantic Services, Service Integration, Semantic Interface (cf., [17,42] for details) and shown useful in, Education [25], Business Intelligence [26], Healthcare [45], Internet of things [59], several branches of Computer Science and Biomedical System [17], Speech and Language Processing [10], spoken document summarization [15], Theoretical Computer Science [20,22,61], and in many more disciplines for details see references of these cited work.…”

Section: Introductionmentioning

confidence: 99%

Generalized rough and fuzzy rough automata for semantic computing

Yadav

Tiwari

Kumari

et al. 2022

Int. J. Mach. Learn. & Cyber.

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The classical automata, fuzzy finite automata, and rough finite state automata are some formal models of computing used to perform the task of computation and are considered to be the input device. These computational models are valid only for fixed input alphabets for which they are defined and, therefore, are less user-friendly and have limited applications. The semantic computing techniques provide a way to redefine them to improve their scope and applicability. In this paper, the concept of semantically equivalent concepts and semantically related concepts in information about real-world applications datasets are used to introduce and study two new formal models of computations with semantic computing (SC), namely, a rough finite-state automaton for SC and a fuzzy finite rough automaton for SC as extensions of rough finite-state automaton and fuzzy finite-state automaton, respectively, in two different ways. The traditional rough finite-state automata can not deal with situations when external alphabet or semantically equivalent concepts are given as inputs. The proposed rough finite-state automaton for SC can handle such situations and accept such inputs and is shown to have successful real-world applications. Similarly, a fuzzy finite rough automaton corresponding to a fuzzy automaton is also failed to process input alphabet different from their input alphabet, the proposed fuzzy finite rough automaton for SC corresponding to a given fuzzy finite automaton is capable of processing semantically related input, and external input alphabet information from the dataset obtained by real-world applications and provide better user experience and applicability as compared to classical fuzzy finite rough automaton.

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Crowdsourced healthcare knowledge creation using patients’ health experience-ontologies

Cited by 5 publications

References 29 publications

Multifaceted Role of Social Media in Healthcare: Opportunities, Challenges, and the Need for Quality Control

Multifaceted Role of Social Media in Healthcare: Opportunities, Challenges, and the Need for Quality Control

Semantic Web and Human Computation: The status of an emerging field

Generalized rough and fuzzy rough automata for semantic computing

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