Named Entity Recognition In Electronic Medical Records Based On Hybrid Neural Network And Transformer
DOI:
https://doi.org/10.59188/eduvest.v4i6.1473Keywords:
Electronic Medical Records, Named Entity Recognition, Information Extraction, Natural Language Processing, Hybrid Neural NetworksAbstract
The development of artificial intelligence in the field of health encourages the use of electronic medical records in all health facilities to record health services provided to patients. For hospitals, extracting information from electronic medical records can make it easier for management to make clinical decisions and for researchers to obtain data for research in the medical and nursing fields. The research builds a model of named entity recognition in electronic medical records based on hybrid neural networks, bidirectional encoder representations from transformers, and setting hyperparameters to get the highest accuracy. The research data set is processed from an initial examination form of an adult patient in a hospital onto an electronic medical record in 2022, and the data is pre-processed. Next, perform the entity-tagging phase on the text and divide 70% of the training datasets by 30% of the testing datasets. Training and evaluation of models built using the confusion matrix method. The results of this study show that the entity identification model called bidirectional encoder representations from transformers consistently outperforms the neural network-based entity recognition model in any evaluation metric. The abbreviation of the bi-directional encoder representation of transformer has very high precision, recall, and f1-score values, demonstrating its ability to recognise entities very well. In this study, although the model named Entity Recognition based on neural networks also has high accuracy, the low precision and recall values indicate that this model may have difficulty recognising entities accurately.
References
Abdillah, A.F., Purwitasari, D., Junita, S., Purnomo, M.H., 2023. Pengenalan Entitas Biomedis dalam Teks Konsultasi Kesehatan Online Berbahasa Indonesia Berbasis Arsitektur Transofmers. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) 10, 131–140. https://doi.org/10.25126/jtiik.2023106337
AI-Ash, H.S., Fanany, I., Bustamam, A., 2019. Indonesian Protected Health Information Removal using Named-Entity Recognition, in: International Conference on Information & Communication Technology and System.
Alsaaran, N., Alrabiah, M., 2021. Classical Arabic Named-Entity Recognition Using Variant Deep Neural Network Architectures and BERT. IEEE Access 9, 91537–91547. https://doi.org/10.1109/ACCESS.2021.3092261
Awalina, A., Bachtiar, F.A., Utaminingrum, F., Korespondensi, P., 2022. Perbandingan Model Transformer pada Deteksi Ulasan Palsu. Jurnal Teknologi Informasi dan Ilmu Komputer 9, 597–604. https://doi.org/10.25126/jtiik.202295696
Berragan, C., Singleton, A., Calafiore, A., Morley, J., 2023. Transformer Based Named-Entity Recognition for Place Name Extraction from Unstructured Text. International Journal of Geographical Information Science 37, 747–766. https://doi.org/10.1080/13658816.2022.2133125
Chen, P., Zhang, M., Yu, X., Li, S., 2022. Named-Entity Recognition of Chinese Electronic Medical Records Based on A Hybrid Neural Network and Medical MC-BERT. BMC Med Inform Decis Mak 22. https://doi.org/10.1186/s12911-022-02059-2
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., 2014. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, in: Conference on Empirical Methods in Natural Language Processing (EMNLP).
Devlin, J., Chang, M.-W., Lee, K., Google, K.T., Language, A.I., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in: Proceedings of NAACL-HLT. pp. 4171–4186.
Dey, R., Salem, F.M., 2017. Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks, in: IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).
Fakhri, M., Putra, D.A., Fathan Hidayatullah, A., Wibowo, A.P., Nastiti, K.R., Yogyakarta, T., Siliwangi, J., Utara, R., Id, A.A., 2023. Named-Entity Recognition of Tourist Destinations Reviews in The Indonesian Language. Jurnal Linguistik Komputasional 6.
Giarsyani, N., Hidayatullah, A.F., Rahmadi, R., 2020. Komparasi Algoritma Machine Learning dan Deep Learning untuk Named-Entity Recognition : Studi Kasus Data Kebencanaan. Jurnal Informatika & Rekayasa Elektronika 3.
Gunawan, W., Suhartono, D., Purnomo, F., Ongko, A., 2018. Named-Entity Recognition for Indonesian Language using BiLSTM-CNNs, in: Procedia Computer Science. Elsevier B.V., pp. 425–432. https://doi.org/10.1016/j.procs.2018.08.193
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S., 2016. Deep Learning for Visual Understanding: A Review. Neurocomputing 187, 27–48. https://doi.org/10.1016/j.neucom.2015.09.116
Haris, M., Pustaka, T., Diponegoro, M.H., Kusumawardani, S., Hidayah, I., 2021. Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning pada Prediksi Kinerja Murid. Jurnal Nasional Teknik Elektro dan Teknologi Informasi | 10.
Hatta Fudholi, D., Abida Nayoan, R.N., Fathan Hidayatullah, A., Brahma Arianto, D., 2022. A Hybrid CNN-BiLSTM Model for Drug Named-Entity Recognition. Journal of Engineering Science and Technology 17, 730–0744.
Hutter, F., Lücke, J., Schmidt-Thieme, L., 2015. Beyond Manual Tuning of Hyperparameters. KI - Kunstliche Intelligenz 29, 329–337. https://doi.org/10.1007/s13218-015-0381-0
Jaariyah, N., Rainarli, E., 2017. Conditional Random Fields untuk Pengenalan Entitas Bernama pada Teks Bahasa Indonesia. Jurnal Ilmiah Komputer dan Informatika 6.
Jati, B.S., Widyawan, S.T., Muhammad Nur Rizal, S.T., 2020. Multilingual Named-Entity Recognition Model for Indonesian Health Insurance Question Answering System, in: International Conference on Information and Communications Technology. Institute of Electrical and Electronics Engineers Inc., pp. 180–184. https://doi.org/10.1109/ICOIACT50329.2020.9332027
Kale, S., Govilkar, S., 2017. Survey of Named-Entity Recognition Techniques for Various Indian Regional Languages. Int J Comput Appl 164, 37–43. https://doi.org/10.5120/ijca2017913621
Kreimeyer, K., Foster, M., Pandey, A., Arya, N., Halford, G., Jones, S.F., Forshee, R., Walderhaug, M., Botsis, T., 2017. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform. https://doi.org/10.1016/j.jbi.2017.07.012
Li, J., Sun, A., Han, J., Li, C., 2022. A Survey on Deep Learning for Named-Entity Recognition. IEEE Trans Knowl Data Eng 34, 50–70. https://doi.org/10.1109/TKDE.2020.2981314
Li, S., Lin, N., Xiao, L., Jiang, S., 2020. IndoAbbr: A New Benchmark Dataset for Indonesian Abbreviation Identification, in: International Conference on Asian Language Processing. Institute of Electrical and Electronics Engineers Inc., pp. 241–246. https://doi.org/10.1109/IALP51396.2020.9310514
Liu, X., Zhou, Y., Wang, Z., 2019. Recognition and Extraction of Named Entities in Online Medical Diagnosis Data Based on a Deep Neural Network. J Vis Commun Image Represent 60, 1–15. https://doi.org/10.1016/j.jvcir.2019.02.001
Luo, L. xia, 2019. Network Text Sentiment Analysis Method Combining LDA Text Representation and GRU-CNN. Pers Ubiquitous Comput 23, 405–412. https://doi.org/10.1007/s00779-018-1183-9
Luthfi, E.T., Izzah, Z., Yusoh, M., Aboobaider, B.M., 2022. BERT Based Named-Entity Recognition for Automated Hadith Narrator Identification. International Journal of Advanced Computer Science and Applications 13.
Munarko, Y., Sutrisno, M.S., Mahardika, W.A.I., Nuryasin, I., Azhar, Y., 2018. Named-Entity Recognition Model for Indonesian Tweet using CRF Classifier, in: IOP Conference Series: Materials Science and Engineering. Institute of Physics Publishing. https://doi.org/10.1088/1757-899X/403/1/012067
Nurchim, N., Nurmalitasari, N., Long, Z.A., 2023. Indonesian News Classification Application with Named-Entity Recognition Approach. Jurnal Infotel 15. https://doi.org/10.20895/infotel.v15i2.909
Otter, D.W., Medina, J.R., Kalita, J.K., 2021. A Survey of the Usages of Deep Learning for Natural Language Processing. IEEE Trans Neural Netw Learn Syst 32, 604–624. https://doi.org/10.1109/TNNLS.2020.2979670
Parsaoran, A., Sitompul, H., 2023. Penggunaan Rekam Medis Elektronik Untuk Pasien Rawat Jalan Di Fasilitas Kesehatan Indonesia : Literature Review. Jurnal Ilmiah Multidisipline 37, 37–46. https://doi.org/10.5281/zenodo.7837905
Peraturan Menteri Kesehatan Republik Indonesia Nomor 24 Tahun 2022 Tentang Rekam Medis, 2022.
Putra, F.N., Fatichah, C., 2018. Klasifikasi Jenis Kejadian Menggunakan Kombinasi neuroNER dan Recurrent Convolutional Neural Network pada Data Twitter. Jurnal Ilmiah Teknologi Sistem Informasi 4, 81–90. https://doi.org/10.26594/register.v4i2.1242
Rachman, V., Savitri, S., Augustianti, F., Mahendra, R., 2020. Named-Entity Recognition on Indonesian Twitter Posts using LSTM Networks, in: International Conference on Advanced Computer Science and Information Systems.
Rashel, F., Luthfi, A., Dinakaramani, A., Manurung, R., 2014. Building an Indonesian Rule-Based Part-of-Speech Tagger, in: Proceedings of the International Conference on Asian Language Processing 2014 (IALP 2014).
Rochmawati, N., Hidayati, H.B., Yamasari, Y., Peni, H., Tjahyaningtijas, A., Yustanti, W., Prihanto, A., 2021. Analisa Learning Rate dan Batch Size pada Klasifikasi Covid menggunakan Deep Learning dengan Optimizer Adam. Journal Information Engineering and Educational Technology 2.
Rothman, D., 2021. Transformers for Natural Language Processing.
Russell, S., Norvig, P., 2010. Artificial Intelligence A Modern Approach.
Sugiyono., 2019. Metodelogi Penelitian Kuantitatif dan Kualitatif dan R&D. Bandung: CV. Alfabeta.
Salminen, J., Hopf, M., Chowdhury, S.A., Jung, S. gyo, Almerekhi, H., Jansen, B.J., 2020. Developing an Online Hate Classifier for Multiple Social Media Platforms. Human-centric Computing and Information Sciences 10. https://doi.org/10.1186/s13673-019-0205-6
Santoso, J., Setiawan, E.I., Yuniarno, E.M., Hariadi, M., Purnomo, M.H., 2020. Hybrid Conditional Random Fields and K-Means for Named-Entity Recognition on Indonesian News Documents. International Journal of Intelligent Engineering and Systems 13, 233–245. https://doi.org/10.22266/IJIES2020.0630.22
Susanty, M., Sukardi, S., 2021. Perbandingan Pre-Trained Word Embedding dan Embedding Layer untuk Named-Entity Recognition Bahasa Indonesia. Jurnal Pengkajian dan Penerapan Teknik Informatika 14, 247–257. https://doi.org/10.33322/petir.v14i2.1164
Tunstall, L., Werra, L. von, Wolf, T., 2022. Natural Language Processing with Transformers.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., 2017. Attention Is All You Need, in: Advances in Neural Information Processing Systems.
Wang, S., Cao, J., 2021. AI and Deep Learning for Urban Computing, in: Urban Book Series. Springer Science and Business Media Deutschland GmbH, pp. 815–844. https://doi.org/10.1007/978-981-15-8983-6_43
Wibawa, M.S., 2016. Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan. Jurnal Sistem dan Informatika 11.
Wintaka, D.C., Bijaksana, M.A., Asror, I., 2019. Named-Entity Recognition on Indonesian Tweets using BiLSTM-CRF, in: Procedia Computer Science. Elsevier B.V., pp. 221–228. https://doi.org/10.1016/j.procs.2019.08.161
Young, T., Hazarika, D., Poria, S., Cambria, E., 2018. Recent Trends in Deep Learning Based Natural Language Processing. IEEE Comput Intell Mag 13, 55–75. https://doi.org/10.1109/MCI.2018.2840738
Yusliani, N., Sufa, M.R.P., Firdaus, A., Abdiansah, Sazaki, Y., 2021. Named-Entity Recognition pada Teks Berbahasa Indonesia menggunakan Metode Hidden Markov Model dan POS-Tagging. Jurnal Linguistik Komputasional 4.
Published
Issue
Section
License
Copyright (c) 2024 Muhammad Sumarudin, Mohammad Syafrullah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
