Review of Extracting Information From the Social Web for Health Personalization
Open accessAbstract
In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The Social Web itself provides many sources of information that can be used to extract information for personalization apart from traditional Web forms and questionnaires. This paper provides a review of different approaches for extracting information from the Social Web for health personalization. We reviewed research literature across different fields addressing the disclosure of health information in the Social Web, techniques to extract that information, and examples of personalized health applications. In addition, the paper includes a discussion of technical and socioethical challenges related to the extraction of information for health personalization.
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Existing studies in emergency access for PHR without blockchain are the traditional system, and some studies based on blockchain do not address access to PHR in an emergency condition.
“These existing studies in emergency access for PHR without blockchain are the traditional system, and also there are some studies based on blockchain but not access to PHR in an emergency condition.”
From EACMS: Emergency Access Control Management System for Personal Health Record Based on Blockchain (2019) · cites this paper as [38]
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Detecting the association of health problems in consumer-level medical text
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Big Data For Health Through Social Media
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Online Health Information Seeking Behaviors of Hispanics in New York City
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Towards Using Social Media to Identify Individuals at Risk for Preventable Chronic Illness
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- Authors
- Fernandez-Luque, L., Karlsen, R., & Bonander, J.
- Affiliations
- Northern Research Institute (NO)
- Centers for Disease Control and Prevention (US)
- ORCID
- Karlsen, Randi
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2011-01-28
- Volume
- 13
- Issue
- 1
- Pages
- e15
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3221336
- OpenAlex
- W2043531425
- Cited by
- 61
- References
- 104
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 1 · 2021: 3 · 2020: 4 · 2019: 6 · 2017: 4 · 2016: 8 · 2015: 6 · 2014: 8 · 2013: 9 · 2012: 6
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Norges Forskningsråd (174934)
- Topics
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Keywords
- Web 2.0, information extraction, health information disclosure, user profiling, health care consumers
- MeSH terms
- Health Education, Humans, Medical Informatics, Natural Language Processing, Online Systems, Social Support, Information Storage and Retrieval, Community Networks, Internet, Data Mining, Electronic Health Records, Health Communication
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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