Classification of medical specialty for text medical report based on natural language processing and deep learning

Hasanen Abdul-Jawad Almuhana; Hawraa Hassan Abbas

doi:10.53730/ijhs.v6nS7.12476

Authors

Hasanen Abdul-Jawad Almuhana Student - College of Engineering. University of Karbala, Iraq
Hawraa Hassan Abbas College of Engineering, University of Karbala, Iraq

Keywords:

Convolution Neural Network (CNN), Natural language processing, health institutions

Abstract

Natural language processing (NLP) is a part of artificial intelligence algorithms that focus on designing and building applications and systems in a way that allows interaction between computers and natural languages developed for human use. NLP has been used in several areas within artificial intelligence and data processing applications such as social media applications and medical applications and translation applications. The medical field is one of the richest in terms of big data, which has not been well invested so far. one of these unused data was the text medical reports. Natural Language Processing analysis is an effective way of examining medical reports and social media data to improve treatments and patient services. by training a computer to make a decision instead of a human, as soon as possible from the decisions of specialist doctors or medical staff working in health institutions to diagnose diseases or Prescribe treatment to the patient. The data of each patient is stored in the form of medical reports mostly as a text document file and kept within the hospital data. Hospitals suffer from a scarcity of medical and nursing staff, in addition to the high cost of hiring medical staff.

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References

M. Tayefi et al., “Challenges and opportunities beyond structured data in analysis of electronic health records,” Wiley Interdiscip. Rev. Comput. Stat., p. e1549, 2021.

R. S. H. Istepanian and T. Al-Anzi, “m-Health 2.0: new perspectives on mobile health, machine learning and big data analytics,” Methods, vol. 151, pp. 34–40, 2018.

Y. Tedla and K. Yamamoto, “Analyzing word embeddings and improving POS tagger of tigrinya,” in 2017 International Conference on Asian Language Processing (IALP), 2017, pp. 115–118.

J. A. Sparks et al., “Rheumatoid arthritis disease activity predicting incident clinically apparent rheumatoid arthritis–associated interstitial lung disease: a prospective cohort study,” Arthritis Rheumatol., vol. 71, no. 9, pp. 1472–1482, 2019.

S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep Learning--based Text Classification: A Comprehensive Review,” ACM Comput. Surv., vol. 54, no. 3, pp. 1–40, 2021.

R. J. Roberts, “PubMed Central: The GenBank of the published literature,” Proceedings of the National Academy of Sciences, vol. 98, no. 2. National Acad Sciences, pp. 381–382, 2001.

J.-D. Kim, T. Ohta, and J. Tsujii, “Corpus annotation for mining biomedical events from literature,” BMC Bioinformatics, vol. 9, no. 1, pp. 1–25, 2008.

W.-C. Kang et al., “Learning to embed categorical features without embedding tables for recommendation,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 840–850.

S. S. S. KS K, “A Survey of Embeddings in Clinical Natural Language Processing,” arXiv Prepr. arXiv1903.01039, 2019.

A. Fesseha, S. Xiong, E. D. Emiru, M. Diallo, and A. Dahou, “Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya,” Information, vol. 12, no. 2, p. 52, 2021.

G. Li, X. Du, X. Li, L. Zou, G. Zhang, and Z. Wu, “Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning,” PeerJ, vol. 9, p. e11262, 2021.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

H. Gholamalinezhad and H. Khosravi, “Pooling methods in deep neural networks, a review,” arXiv Prepr. arXiv2009.07485, 2020.

X. Zhang, J. Zhao, and Y. LeCun, “Character-level convolutional networks for text classification,” Adv. Neural Inf. Process. Syst., vol. 28, 2015.

R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629, 2018.

R. Ghosh and A. K. Gupta, “Scale steerable filters for locally scale-invariant convolutional neural networks,” arXiv Prepr. arXiv1906.03861, 2019.

S. Wu et al., “Deep learning in clinical natural language processing: a methodical review,” J. Am. Med. Informatics Assoc., vol. 27, no. 3, pp. 457–470, 2020.

M. Hughes, I. Li, S. Kotoulas, and T. Suzumura, “Medical text classification using convolutional neural networks,” in Informatics for Health: Connected Citizen-Led Wellness and Population Health, IOS Press, 2017, pp. 246–250.

J. Geraci, P. Wilansky, V. de Luca, A. Roy, J. L. Kennedy, and J. Strauss, “Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression,” Evid. Based. Ment. Health, vol. 20, no. 3, pp. 83–87, 2017.

W.-H. Weng, K. B. Wagholikar, A. T. McCray, P. Szolovits, and H. C. Chueh, “Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach,” BMC Med. Inform. Decis. Mak., vol. 17, no. 1, pp. 1–13, 2017.

X. Mu and H. Zhang, “Embedded electronic medical record text data mining using neural network association classification algorithm,” Expert Syst., p. e12874, 2021.

A. A. Thomas et al., “Extracting data from electronic medical records: validation of a natural language processing program to assess prostate biopsy results,” World J. Urol., vol. 32, no. 1, pp. 99–103, 2014.

J. Hammoud, A. Vatian, N. Dobrenko, N. Vedernikov, A. Shalyto, and N. Gusarova, “New Arabic Medical Dataset for Diseases Classification,” arXiv Prepr. arXiv2106.15236, 2021.

Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2022). Post-pandemic health and its sustainability: Educational situation. International Journal of Health Sciences, 6(1), i-v. https://doi.org/10.53730/ijhs.v6n1.5949

M. Khachidze, M. Tsintsadze, and M. Archuadze, “Natural language processing based instrument for classification of free text medical records,” Biomed Res. Int., vol. 2016, 2016.

G. Neha and P. Mathur, “Spell checking techniques in NLP: a survey,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 2, no. 12, 2012.