Learning structured medical information from social media.
Our goal is to summarise and aggregate information from social media regarding the symptoms of a disease, the drugs used and the treatment effects both positive and negative. To achieve this we first apply a supervised machine learning method to automatically extract medical concepts from natural la...
| Published in: | Journal of Biomedical Informatics Vol. 110 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Published: |
Academic Press Inc.
Oct2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146481897&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146481897 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Oct2020 vid: 110 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 146481897 146481897 NLM32942027 10.1016/j.jbi.2020.103568 NLM32942027 146481897 ppct: 1 formats: tig: atl: Learning structured medical information from social media. aug: au: Hasan, Abul Levene, Mark Weston, David affil: Department of Computer Science and Information Systems, Birkbeck, University of London, London WC1E 7HX, United Kingdom sug: subj: Social Media Algorithms Language Social Readjustment Rating Scale ab: Our goal is to summarise and aggregate information from social media regarding the symptoms of a disease, the drugs used and the treatment effects both positive and negative. To achieve this we first apply a supervised machine learning method to automatically extract medical concepts from natural language text. In an environment such as social media, where new data is continuously streamed, we need a methodology that will allow us to continuously train with the new data. To attain such incremental re-training, a semi-supervised methodology is developed, which is capable of learning new concepts from a small set of labelled data together with the much larger set of unlabelled data. The semi-supervised methodology deploys a conditional random field (CRF) as the base-line training algorithm for extracting medical concepts. The methodology iteratively augments to the training set sentences having high confidence, and adds terms to existing dictionaries to be used as features with the base-line model for further classification. Our empirical results show that the base-line CRF performs strongly across a range of different dictionary and training sizes; when the base-line is built with the full training data the F1 score reaches the range 84%-90%. Moreover, we show that the semi-supervised method produces a mild but significant improvement over the base-line. We also discuss the significance of the potential improvement of the semi-supervised methodology and found that it is significantly more accurate in most cases than the underlying base-line model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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