Pembangkitan interpretasi tekstual berbahasa Indonesia berdasarkan data pemeriksaan kimia darah menggunakan pendekatan berbasis r-template

Textual interpretation generation in Indonesian language based on blood chemistry tests data using r-template based approach

*Indra Aulia orcid scopus  -  Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia
Sarah Purnamawati  -  Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia
Junianto Junianto  -  Department of Information Technology, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia
Received: 31 Mar 2019; Revised: 1 Nov 2019; Accepted: 5 Nov 2019; Published: 31 Jan 2020; Available online: 14 Nov 2019.
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Section: Original Research Articles
Language: ID
Statistics: 286 93
Abstract
The result of the blood chemistry tests is usually presented in the form of a table written in abbreviations, numbers, and units. Unfortunately, the young doctors often require the time and experience for interpreting the blood chemistry tests into a textual representation, which is easy to read and understand. Therefore, this research aimed at developing a system (BTISys) that can generate the textual representation in the Indonesian language automatically based on the blood chemistry test. BTISys generates the representation using Natural Language Generation (NLG) approach based on the r-template method. The reliability of BTISys is measured by considering the naturalness of generated textual representation. The naturalness can be observed by three criteria, such as readability, clarity, and general appropriateness. The reliability of BTISys is quite good to generate the textual representation automatically. It can be seen from the readability, clarity, and general appropriateness, which reach 73 %, 70 %, and 60 % respectively, that implies the naturalness of generated textual representation.
Keywords: blood chemistry tests; natural language generation; template based; naturalness; Indonesian language

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  1. National Heart, Lung, and Blood Institute, “Blood tests,” NHBLI, 2012. [Online]. Available: https://www.nhlbi.nih.gov/health/health-topics/topics/bdt. [Accessed: 2 Dec. 2018].
  2. I. Adeyanju, “Generating weather forecast texts with case based reasoning,” International Journal of Computer Applications, vol. 45, no. 10, pp. 35-40, 2012. doi: 10.5120/6819-9176
  3. M. Molina, J. Sanchez-Soriano, and O. Corcho, “Using open geographic data to generate natural language descriptions for hydrological sensor networks,” Sensors, vol. 15, no. 7, pp. 16009-16026, 2015. doi: 10.3390/s150716009
  4. J. Mahapatra, S. K. Naskar, and S. Bandyopadhyay, “Statistical natural language generation from tabular non-textual data,” in 9th International Natural Language Generation Conference, Edinburg, UK, Sept. 2016, pp. 143-153.
  5. A. Gatt et al., “From data to text in the neonatal intensive care unit: Using NLG technology for decision support and information management,” AI Communications, vol. 22, no. 3, pp. 153-186, 2009. doi: 10.3233/AIC-2009-0453
  6. B. Di Eugenio et al., “PatientNarr: towards generating patient-centric summaries of hospital stays,” in 8th International Natural Language Generation Conference, Philadelphia, USA, Jun. 2014, pp. 6-10. doi: 10.3115/v1/W14-4402
  7. W. Pratomo and A. M. Barmawi, “Performing chart interpretation using lexical selection in Indonesian language,” in 2013 International Conference on Advanced Computer Science and Information Systems, Bali, Indonesia, Sept. 2013, pp. 199-205. doi: 10.1109/ICACSIS.2013.6761576
  8. I. Aulia and A. M. Barmawi, “An automatic health surveillance chart interpretation system based on Indonesian language,” in 2015 International Conference on Advanced Computer Science and Information Systems, Depok, Indonesia, Oct. 2015, pp. 163-170. doi: 10.1109/ICACSIS.2015.7415165
  9. Kementerian Kesehatan Republik Indonesia, Pedoman interpretasi data klinik. Jakarta: Kementerian Kesehatan Republik Indonesia, 2011.
  10. A. Belz and E. Reiter, “Comparing automatic and human evaluation of NLG systems,” in 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy, Apr. 2006, pp. 213-320.
  11. R. Vadlapudi and R. Katragadda, “On automated evaluation of readability of summaries: capturing grammaticality, focus, structure and coherence,” in NAACL HLT 2010 Student Research Workshop, Los Angeles, USA, Jun. 2010, pp. 7-12.
  12. K. Willems and L. D. Cuypere, Naturalness and iconicity in language. John Benjamins Publishing, 2008.
  13. E. Reiter and S. Sripada, “Should corpora texts be gold standards for NLG?,” in International Natural Language Generation Conference, New York, USA, Jul. 2002, pp. 97-104.
  14. J. Sinclair, “Naturalness in language,” Ilha do Desterro, vol. 5, no. 11, pp. 203-210, 1984.
  15. Boston University School of Public Health, “Atherosclerosis,” 2016. [Online]. Available: http://sphweb.bumc.bu.edu/otlt/MPH-Modules/PH/PH709_Heart/. [Accessed: 4 Jan. 2019].

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