Automatic identification of high impact relevant articles to support clinical decision making using attention-based deep learning

Beomjoo Park; Muhammad Afzal; Jamil Hussain; Asim Abbas; Sungyoung Lee

doi:10.3390/electronics9091364

Automatic identification of high impact relevant articles to support clinical decision making using attention-based deep learning

Beomjoo Park
, Muhammad Afzal
, Jamil Hussain
, Asim Abbas
, Sungyoung Lee^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (SciVal)

Original language	English
Article number	1364
Pages (from-to)	1-15
Number of pages	15
Journal	Electronics (Switzerland)
Volume	9
Issue number	9
DOIs	https://doi.org/10.3390/electronics9091364
Publication status	Published (VoR) - Sept 2020

Funding

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information and communications Technology Promotion). This work was supported by the Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655), NRF-2016K1A3A7A03951968, and NRF-2019R1A2C2090504. Funding: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information and communications Technology Promotion). This work was supported by the Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655), NRF-2016K1A3A7A03951968, and NRF-2019R1A2C2090504.

Keywords

Clinical decision support
Deep learning
Health communication
Health management
Healthcare
Machine learning
Precision medicine

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10.3390/electronics9091364

Cite this

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title = "Automatic identification of high impact relevant articles to support clinical decision making using attention-based deep learning",

keywords = "Clinical decision support, Deep learning, Health communication, Health management, Healthcare, Machine learning, Precision medicine",

author = "Beomjoo Park and Muhammad Afzal and Jamil Hussain and Asim Abbas and Sungyoung Lee",

note = "Funding Information: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information and communications Technology Promotion). This work was supported by the Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655), NRF-2016K1A3A7A03951968, and NRF-2019R1A2C2090504. Funding Information: Funding: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information and communications Technology Promotion). This work was supported by the Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655), NRF-2016K1A3A7A03951968, and NRF-2019R1A2C2090504. Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2020",

month = sep,

doi = "10.3390/electronics9091364",

language = "English",

volume = "9",

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journal = "Electronics (Switzerland)",

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T1 - Automatic identification of high impact relevant articles to support clinical decision making using attention-based deep learning

AU - Park, Beomjoo

AU - Afzal, Muhammad

AU - Hussain, Jamil

AU - Abbas, Asim

AU - Lee, Sungyoung

N1 - Funding Information: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information and communications Technology Promotion). This work was supported by the Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655), NRF-2016K1A3A7A03951968, and NRF-2019R1A2C2090504. Funding Information: Funding: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-0-01629) supervised by the IITP (Institute for Information and communications Technology Promotion). This work was supported by the Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00655), NRF-2016K1A3A7A03951968, and NRF-2019R1A2C2090504. Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2020/9

Y1 - 2020/9

KW - Clinical decision support

KW - Deep learning

KW - Health communication

KW - Health management

KW - Healthcare

KW - Machine learning

KW - Precision medicine

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UR - https://www.scopus.com/pages/publications/85090539290#tab=citedBy

U2 - 10.3390/electronics9091364

DO - 10.3390/electronics9091364

M3 - Article

AN - SCOPUS:85090539290

SN - 2079-9292

VL - 9

SP - 1

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JO - Electronics (Switzerland)

JF - Electronics (Switzerland)

IS - 9

M1 - 1364

ER -

Automatic identification of high impact relevant articles to support clinical decision making using attention-based deep learning

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Keywords

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