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Symptoms identification of ICD-11 based on clinical NLP mobile apps for diagnosing the disease (ICD-11)


Introduction: There are still many people in Indonesia who are not aware of the importance of information related to the early symptoms that must be experienced when they become patients. Not infrequently, this lack of information disclosure results in misdiagnosis and even leads to unexpected death. Anamnesis is a process where the doctor or medical record nurse gives several questions about the clinical pathway in the form of a narrative to facilitate early identification of the disease, and the results of this history-taking process are stored in the Electronic Medical Record (EMR). EMR narratives often cannot be processed by computers if language literacy is not standardized or ambiguous, so the need to overcome this problem requires the use of technology to minimize misdiagnosis and facilitate the identification process by developing digitization in the form of mobile applications that are integrated with Natural Language Processing technology and ICD-11 in the symptom identification process. This study aims to identify ICD-11 symptoms based on clinical NLP mobile application to diagnose the disease (ICD-11).

Methods: The applications of Natural language processing includes literature study, Voice Recognition, Tokenization, Stemming, The process of Stopwords Removal, Named Entity Recognition, Data Translation, Access ICD Data, and Mobile User Interfaces.

Results: Named Entity Recognition (NER) is used to identify symptoms of digestive system diseases, with an accuracy rate of 74.3%. In stemming and stopwords processing, the NLP accuracy rates are 95.9% and 97.2%, respectively.

Conclusions: This research focuses on the application mobile and development of the Named Entity Recognition (NER) model. The importance of the NLP process in the development of information, especially for word processing, aims as a device that simplifies speech recognition systems to be more helpful.


  1. Gaebel W, Stricker J, Riesbeck M, Zielasek J, Kerst A, Meisenzahl-Lechner E, et al. Accuracy of diagnostic classification and clinical utility assessment of ICD-11 compared to ICD-10 in 10 mental disorders: findings from a web-based field study. Eur Arch Psychiatry Clin Neurosci. 2020;270(3):281–9.
  2. Li J, Deng L, Haeb-Umbach R, Gong Y. Robust automatic speech recognition: A bridge to practical applications. 2015. 1–286 p.
  3. Santra S, Bhowmick S, Paul A, Chatterjee P, Deyasi A. Development of GUI for Text-to-Speech Recognition using Natural Language Processing. In: 2018 2nd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). 2018. p. 1–4.
  4. Reshamwala A, Mishra D, Pawar P. IRACST Eng. Sci. Technol. An Int. J. None. 2013;3(1):113–116.
  5. Pocai B. The ICD-11 has been adopted by the World Health Assembly. World Psychiatry. 2019;18:371.
  6. Solangi YA, Solangi ZA, Aarain S, Abro A, Mallah GA, Shah A. Review on natural language processing (NLP) and its toolkits for opinion mining and sentiment analysis. In: 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS). 2018. p. 1–4.
  7. Rizki AS, Tjahyanto A, Trialih R. Comparison of stemming algorithms on Indonesian text processing. Telkomnika. 2019;17(1).
  8. Gerlach M, Shi H, Amaral LAN. A universal information theoretic approach to the identification of stopwords. Nat Mach Intell. 2019;1(12):606–12.
  9. Lignos C, Kamyab M. If You Build Your Own NER Scorer, Non-replicable Results Will Come. In: Proceedings of the First Workshop on Insights from Negative Results in NLP. 2020. p. 94–9.
  10. Wibawa AS, Purwarianti A. Indonesian named-entity recognition for 15 classes using ensemble supervised learning. Procedia Comput Sci. 2016;81:221–8.
  11. Silalahi M, Cahyani DE, Sensuse DI, Budi I. Developing indonesian medicinal plant ontology using socio-technical approach. In: 2015 International Conference on Computer, Communications, and Control Technology (I4CT). IEEE; 2015. p. 39–43.
  12. Wibisono Y, Khodra ML. Pengenalan entitas bernama otomatis untuk Bahasa Indonesia dengan pendekatan pembelajaran mesin. 2018;
  13. Névéol A, Robert A, Anderson R, Cohen KB, Grouin C, Lavergne T, et al. CLEF eHealth 2017 Multilingual Information Extraction task Overview: ICD10 Coding of Death Certificates in English and French. In: CLEF (Working Notes). 2017.
  14. Muslim A, Mutiara AB, Suhendra A, Oswari T. Expert mapping development system with disease searching sympthom based on ICD 10. In: 2018 Third International Conference on Informatics and Computing (ICIC). IEEE; 2018. p. 1–4.
  15. Putra FB, Yusuf AA, Yulianus H, Pratama YP, Humairra DS, Erifani U, et al. Identification of Symptoms Based on Natural Language Processing (NLP) for Disease Diagnosis Based on International Classification of Diseases and Related Health Problems (ICD-11). In: 2019 International Electronics Symposium (IES). 2019. p. 1–5.
  16. He Y, Sainath TN, Prabhavalkar R, McGraw I, Alvarez R, Zhao D, et al. Streaming end-to-end speech recognition for mobile devices. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019. p. 6381–5.

How to Cite

Budiarti, R. P. N., Sritrusta Sukaridhoto, Ilham Achmad Al-Hafidz, & Naufal Adi Satrio. (2022). Symptoms identification of ICD-11 based on clinical NLP mobile apps for diagnosing the disease (ICD-11). Bali Medical Journal, 11(3), 1162–1167.




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Rizqi Putri Nourma Budiarti
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Sritrusta Sukaridhoto
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Ilham Achmad Al-Hafidz
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Naufal Adi Satrio
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