A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums.
Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access pat...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 5; pp. 1 - 9 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143018935&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143018935 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2020 vid: 44 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143018935 143018935 143018935 10.1007/s10916-020-01547-0 143018935 ppf: 1 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums. aug: au: Jelodar, Hamed Wang, Yongli Rabbani, Mahdi Xiao, Gang Zhao, Ruxin affil: School of Computer Science and Technology, Nanjing University of Science and Technology, 210094, Nanjing, China sug: subj: Social Media Medical Records Semantic Analysis Conceptual Framework Health Information Management Alcoholic Beverages Collaboration Random Forest Data Mining Natural Language Processing Health Knowledge Algorithms Patient Safety Information Retrieval Data Management ab: Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access patients' opinions, as well as discussions between patients. Recently, the data processing of the health community and, how to extract knowledge is a significant technical challenge. There are many online group and forums that users can discuss on healthcare issues. Therefore, we can examine these text documents for discovering knowledge and evaluating patients' behavior based on their opinions and discussions. For example, there are many questions and answering groups on Twitter or Facebook. Given the importance of the research, in this paper, we present a semantic framework based on topic model (LDA) and Random forest(RF) to predict and retrieval latent topics of healthcare text-documents from an online forum. We extract our healthcare records (patient-questions) from patient.info website as a real dataset. Experiments on our dataset show that social media forums could help for detecting significant patient safety problems on healthcare issues. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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