Utilizing Twitter data for analysis of chemotherapy.
Objective: Twitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets.Materia...
| Publicado en: | International Journal of Medical Informatics Vol. 120; pp. 92 - 101 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Dec2018
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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=132855339&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132855339 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Dec2018 vid: 120 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 132855339 132855339 NLM30409350 10.1016/j.ijmedinf.2018.10.002 NLM30409350 132855339 ppf: 92 ppct: 9 formats: tig: atl: Utilizing Twitter data for analysis of chemotherapy. aug: au: Zhang, Ling Hall, Magie Bastola, Dhundy affil: School of Interdisciplinary Informatics, University of Nebraska at Omaha, United States sug: subj: Health Behavior Social Media Adverse Drug Event Prevention and Control Neoplasms Psychosocial Factors Neoplasms Drug Therapy Antineoplastic Agents Therapeutic Use Communication Methods Scales ab: Objective: Twitter has become one of the most popular social media platforms that offers real-world insights to healthy behaviors. The purpose of this study was to assess and compare perceptions about chemotherapy of patients and health-care providers through analysis of chemo-related tweets.Materials and Methods: Cancer-related Twitter accounts and their tweets were obtained through using Tweepy (Python library). Multiple text classification algorithms were tested to identify the models with best performance in classifying the accounts into individual and organization. Chemotherapy-specific tweets were extracted from historical tweetset, and the content of these tweets was analyzed using topic model, sentiment analysis and word co-occurrence network.Results: Using the description in Twitter users' profiles, the accounts related with cancer were collected and coded as individual or organization. We employed Long Short Term Memory (LSTM) network with GloVe word embeddings to identify the user into individuals and organizations with accuracy of 85.2%. 13, 273 and 14,051 publicly available chemotherapy-related tweets were retrieved from individuals and organizations, respectively. The content of the chemo-related tweets was analyzed by text mining approaches. The tweets from individual accounts pertained to personal chemotherapy experience and emotions. In contrast with the personal users, professional accounts had a higher proportion of neutral tweets about side effects. The information about the assessment of response to chemotherapy was deficient from organizations on Twitter.Discussion: Examining chemotherapy discussions on Twitter provide new lens into content and behavioral patterns associated with treatments for cancer patients. The methodology described herein allowed us to collect relatively large number of health-related tweets over a greater time period and exploit the potential power of social media, which provide comprehensive view on patients' perceptions of chemotherapy.Conclusion: This study sheds light on using Twitter data as a valuable healthcare data source for helping oncologists (organizations) in understanding patients' experiences while undergoing chemotherapy, in developing personalize therapy plans, and a supplement to the clinical electronic medical records (EMRs). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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