1. E. H. Houssein, R. E. Mohamed and A. A. Ali, "Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review," in IEEE Access, vol. 9,pp.140628-140653,2021,
2. doi: 10.1109/ACCESS.2021.3119621.
3. Meleet, Merin, G. N. Srinivasan, and Nagaraj G. Cholli. "Entity Recognition in Clinical Text Using A Hybrid Model Based on LSTM and Neural Networks." 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2022.
4. Reimer, Andrew P., et al. "Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models." International Journal of Medical Informatics 156 (2021): 104588.
5. Amitha and Meleet, M, A System for Recommendation of Medication Using Gaussian Naïve Bayes Classifier,  IJIRCST Vol-7 Issue-3 Page No-100-103 (ISSN 2347 - 5552)
6. Chalapathy, Raghavendra, Ehsan Zare Borzeshi, and Massimo Piccardi. "Bidirectional LSTM-CRF for clinical concept extraction." arXiv preprint arXiv:1611.08373 (2016).
7. Mei, j., Zhao, S., Jin, F., Xia, E., Liu, H., Li, X.: Deep diabetologist: learning to prescribe hypoglycemia medications with hierarchical recurrent neural networks. arXiv. https://arxiv. org/pdf/1810.07692.pdf
8. Choi, E., Schuetz, A., Steward, W.F., Sun, J.: Medical concept representation learning fromelectronic health records and its application on heart failure prediction. arXiv. https://arxiv.org/abs/1602.03686 (2017)
9. Liang, Z., Zhang, G., Huang, X., Hu, Q.: Deep learning for healthcare decision making with EMRs. In: Proceedings of 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 556–559
10. Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S. Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine. Omics: a journal of integrative biology. 2018;22:630-6.
11. Pham T, Tran T, Phung D, Venkatesh S. Predicting healthcare trajectories from medical records: A deep learning approach. Journal of biomedical informatics. 2017;69:218-29.
12.  Guo, Aixia, et al. "Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models." BMC Medical Informatics and Decision Making 21.1 (2021): 1-10.
13.  Rasmy, L., Zhu, J., Li, Z., Hao, X., Tran, H. T., Zhou, Y., ... & Zhi, D. (2021). Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data. arXiv preprint arXiv:2110.00998.
14. Takahashi, Kanae, Kouji Yamamoto, Aya Kuchiba, and Tatsuki Koyama. "Confidence interval for micro-averaged F1 and macro-averaged F1 scores." Applied Intelligence 52, no. 5 (2022): 4961-4972.
15.  Carrington, André M., Douglas G. Manuel, Paul W. Fieguth, Tim Ramsay, Venet Osmani, Bernhard Wernly, Carol Bennett et al. "Deep roc analysis and auc as balanced average accuracy to improve model selection, understanding and interpretation." arXiv preprint arXiv:2103.11357 (2021).
16. Squarcina, Letizia, Filippo Maria Villa, Maria Nobile, Enrico Grisan, and Paolo Brambilla. "Deep learning for the prediction of treatment response in depression." Journal of Affective Disorders 281 (2021): 618-622

Assistant Professor, Department of Information Science and Engineering, R V College of Engineering, Bangalore, India