Word-level text highlighting of medical texts for telehealth services.
The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This creates additional workload for medical professio...
| Publicado en: | Artificial Intelligence in Medicine Vol. 127 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
May2022
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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=156286412&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156286412 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: May2022 vid: 127 pid: 1004 pub: Elsevier B.V. artinfo: ui: 156286412 156286412 NLM35430043 156286412 10.1016/j.artmed.2022.102284 NLM35430043 156286412 ppct: 1 formats: tig: atl: Word-level text highlighting of medical texts for telehealth services. aug: au: Ozyegen, Ozan Kabe, Devika Cevik, Mucahit affil: Data Science Lab at Ryerson University, Toronto, ON M5B 1G3, Canada sug: subj: Telemedicine Natural Language Processing Clinical Assessment Tools Arthritis Impact Measurement Scales Ferrans and Powers Quality of Life Index Human ab: The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This creates additional workload for medical professionals who are heavily dependent on medical data to complete their research and consult their patients. This paper aims to show how different text highlighting techniques can capture relevant medical context. This would reduce the doctors' cognitive load and response time to patients by facilitating them in making faster decisions, thus improving the overall quality of online medical services. Three different word-level text highlighting methodologies are implemented and evaluated. The first method uses Term Frequency - Inverse Document Frequency (TF-IDF) scores directly to highlight important parts of the text. The second method is a combination of TF-IDF scores, Word2Vec and the application of Local Interpretable Model-Agnostic Explanations to classification models. The third method uses neural networks directly to make predictions on whether or not a word should be highlighted. Our numerical study shows that the neural network approach is successful in highlighting medically-relevant terms and its performance is improved as the size of the input segment increases. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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