Why Medical Schools Should Embrace Wikipedia: Final-Year Medical Student Contributions to Wikipedia Articles for Academic Credit at One School

Azzam, Amin; Bresler, David; León, Armando García de; Maggio, Lauren A.; Whitaker, Evans; Heilman, James; Orlowitz, Jake; Swisher, Valerie; Rasberry, Lane; Otoide, Kingsley; Trotter, Fred; Ross, Will; McCue, Jack D.

doi:10.1097/acm.0000000000001381

Cited by 82 publications

(59 citation statements)

References 3 publications

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“…There are currently many initiatives that aim to ensure that the editions in Wikipedia related articles are of high quality (Azer, 2014;Hodson, 2015;Matheson & Matheson, 2017;Murray, 2019). Some, for example, have allowed senior medical practitioners to edit some Wikipedia articles, resulting in more stable and high quality texts (Moturu & Liu, 2009;Head & Eisenberg, 2010;Cohen, 2013;Hasty et al, 2014;Temple & Fraser, 2014;Farič & Potts, 2014;Azer, 2015;Azzam et al, 2017a;Shafee et al, 2017;Sciascia & Radin, 2017;Brigo & Erro, 2018;Del Valle et al, 2018).…”

Section: Wikipediamentioning

confidence: 99%

DISNET: a framework for extracting phenotypic disease information from public sources

García

Rodríguez‐González

Santamaría

et al. 2020

PeerJ

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Background Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows one not only to complement and merge medical knowledge but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (http://disnet.ctb.upm.es/), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. Methods We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. Results We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system’s API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. Discussion DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system’s reliability.

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Section: Wikipediamentioning

confidence: 99%

DISNET: a framework for extracting phenotypic disease information from public sources

García

Rodríguez‐González

Santamaría

et al. 2020

PeerJ

View full text Add to dashboard Cite

show abstract

“…There are currently many initiatives that aim to ensure that the editions in Wikipedia related articles are of high quality (Azer, 2014a;Hodson, 2015;Matheson & Matheson, 2017;Murray, 2019). Some, for example, have allowed senior medical practitioners to edit some Wikipedia articles, resulting in more stable and high quality texts (Moturu & Liu, 2009;Head & Eisenberg, 2010;Cohen, 2013a;Hasty et al, 2014;Temple & Fraser, 2014;Farič & Potts, 2014;Azer, 2015;Azzam et al, 2017a;Aibar, 2017;Shafee et al, 2017a;Sciascia & Radin, 2017;Brigo & Erro, 2018;Valle et al, 2018).…”

Section: Wikipediamentioning

confidence: 99%

“…Wikipedia contains more than 155,000 articles in the field of medicine (Azzam et al, 2017b) and is one of the most widely used medical sources (Friedlin & McDonald, 2010) by the general community (Aibar, 2017) and also by medical specialists (Azer, 2014b;Shafee et al, 2017b), the latter ones having deeply been involved in its enrichment (Azzam et al, 2017b) (Cohen, 2013b). One of the initiatives is the Cochrane/Wikipedia, which aims at increasing reliability in articles with medical content (Matheson & Matheson, 2017).…”

Section: Wikipediamentioning

confidence: 99%

Peer Review #1 of "DISNET: a framework for extracting phenotypic disease information from public sources (v0.1)"

2020

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Background. Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows, not only to complement and merge medical knowledge, but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (disnet.ctb.upm.es), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. Methods. We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. Results. We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system's API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. Discussion. DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system's reliability.

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“…13 And some medical education courses introduce students to the editing and critical appraisal of Wikipedia health articles. 14,15 The goal is to further develop this public resource as a shared health benefit, while giving students a sense of how new medical knowledge is built through the accumulation and review of new studies. This approach has the added bonus of increasing students' familiarity with the structure and nature of the increasingly available biomedical literature that may benefit their patients.…”

Section: Implications Of Open Access On Medical Educationmentioning

confidence: 99%

Is Medical Education Ready for Universal Open Access to Research?

Willinsky

Maggio

2019

Journal of Graduate Medical Education

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Why Medical Schools Should Embrace Wikipedia: Final-Year Medical Student Contributions to Wikipedia Articles for Academic Credit at One School

Abstract: Supplemental Digital Content is available in the text.

Cited by 82 publications

References 3 publications

DISNET: a framework for extracting phenotypic disease information from public sources

DISNET: a framework for extracting phenotypic disease information from public sources

Peer Review #1 of "DISNET: a framework for extracting phenotypic disease information from public sources (v0.1)"

Is Medical Education Ready for Universal Open Access to Research?

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