@article{
 title = {The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions},
 type = {article},
 year = {2013},
 pages = {e14},
 volume = {2},
 websites = {http://www.researchprotocols.org/2013/1/e14/},
 month = {4},
 day = {5},
 id = {4d66f145-86f5-39d8-a4f0-32de018507e4},
 created = {2013-03-06T10:37:55.000Z},
 accessed = {2013-04-06},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Vandervalk2013},
 folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},
 private_publication = {false},
 abstract = {Background: The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. Objective: The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. Methods: We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. Results: A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. Conclusions: The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies.},
 bibtype = {article},
 author = {Vandervalk, Ben and McCarthy, E. Luke and Cruz-Toledo, José and Klein, Artjom and Baker, Christopher J. O. and Dumontier, Michel and Wilkinson, Mark D.},
 doi = {10.2196/resprot.2315},
 journal = {JMIR Research Protocols},
 number = {1}
}

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Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. Objective: The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. Methods: We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. Results: A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. Conclusions: The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies.","bibtype":"article","author":"Vandervalk, Ben and McCarthy, E. Luke and Cruz-Toledo, José and Klein, Artjom and Baker, Christopher J. O. and Dumontier, Michel and Wilkinson, Mark D.","doi":"10.2196/resprot.2315","journal":"JMIR Research Protocols","number":"1","bibtex":"@article{\n title = {The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions},\n type = {article},\n year = {2013},\n pages = {e14},\n volume = {2},\n websites = {http://www.researchprotocols.org/2013/1/e14/},\n month = {4},\n day = {5},\n id = {4d66f145-86f5-39d8-a4f0-32de018507e4},\n created = {2013-03-06T10:37:55.000Z},\n accessed = {2013-04-06},\n file_attached = {false},\n profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},\n last_modified = {2017-03-22T07:45:59.566Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Vandervalk2013},\n folder_uuids = {ec2ec352-fa41-43db-919d-f96649955c40},\n private_publication = {false},\n abstract = {Background: The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. 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Methods: We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. Results: A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. Conclusions: The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies.},\n bibtype = {article},\n author = {Vandervalk, Ben and McCarthy, E. Luke and Cruz-Toledo, José and Klein, Artjom and Baker, Christopher J. 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