Natural Language Processing utilization in Healthcare
Syihaabul Hudaa1, Dwi Bambang Putut Setiyadi2, E. Laxmi Lydia3, K. Shankar4, Phong Thanh Nguyen5, Wahidah Hashim6, Andino Maseleno7
1Syihaabul Hudaa, Ahmad Dahlan University of Technology and Business, Indonesia.
2Dwi Bambang Putut Setiyadi, Universitas Widya Dharma Klaten, Indonesia.
3E. Laxmi Lydia, Professor, Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A),Visakhapatnam (Andhra Pradesh), India.
4K. Shankar, Department of Computer Applications, Alagappa University, Karaikudi, India.
5Phong Thanh Nguyen, Department of Project Management, Ho Chi Minh City Open University, Vietnam.
6Wahidah Hashim, Institute of Informatics and Computing Energy, Universiti Tenaga Nasional, Malaysia.
7Andino Maseleno, Institute of Informatics and Computing Energy, Universiti Tenaga Nasional, Malaysia.
Manuscript received on 15 September 2019 | Revised Manuscript received on 24 September 2019 | Manuscript Published on 10 October 2019 | PP: 1117-1120 | Volume-8 Issue-6S2, August 2019 | Retrieval Number: F13050886S219/19©BEIESP | DOI: 10.35940/ijeat.F1305.0886S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The significance of consolidating Natural Language Processing (NLP) techniques in clinical informatics research has been progressively perceived over the previous years, and has prompted transformative advances. Ordinarily, clinical NLP frameworks are created and assessed on word, sentence, or record level explanations that model explicit traits and highlights, for example, archive content (e.g., persistent status, or report type), record segment types (e.g., current meds, past restorative history, or release synopsis), named substances and ideas (e.g., analyses, side effects, or medicines) or semantic qualities (e.g., nullification, seriousness, or fleetingness). While some NLP undertakings consider expectations at the individual or gathering client level, these assignments still establish a minority. Here we give an expansive synopsis and layout of the difficult issues engaged with characterizing suitable natural and outward assessment strategies for NLP look into that will be utilized for clinical results research, and the other way around. A specific spotlight is set on psychological wellness investigate, a zone still generally understudied by the clinical NLP look into network, however where NLP techniques are of prominent importance. Ongoing advances in clinical NLP strategy improvement have been huge, yet we propose more accentuation should be put on thorough assessment for the field to progress further. To empower this, we give noteworthy recommendations, including an insignificant convention that could be utilized when announcing clinical NLP strategy improvement and its assessment.
Keywords: NLP, Artificial Intelligence, HER.
Scope of the Article: Healthcare Informatics