A Machine Learning Model for Clinical Decision Support for Drug Recommendation
DOI:
https://doi.org/10.55524/Keywords:
Clinical Decision Support System, Electronic Health Record(EHR), Long Short Term Memory(LSTM), Machine Learning, Natural Language Processing, Recurrent Neural Networks (RNN)Abstract
Modern machine learning techniques plays a very crucial role in dealing with very complex unstructured data that is available in the medical domain. The wide range of applications in this area is capable of changing the available data to valuable information that could be used for recommendation of appropriate treatment and drugs by analysing the symptoms and other information regarding the patient . In this work, the data available in the form of plain text in the form of electronic health records were used to give appropriate recommendation regarding the medication that could be given to the patient. Thus it acts as a clinical decision support system that can assist the doctor in taking suitable decisions regarding the treatment plan of the patient. The model used techniques from Natural Language processing and Deep Learning to process that raw data and build a learning model for recommendation. The model was able to give an accuracy of 77 percent with raw text as input.
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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,
doi: 10.1109/ACCESS.2021.3119621.
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.
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.
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)
Chalapathy, Raghavendra, Ehsan Zare Borzeshi, and Massimo Piccardi. "Bidirectional LSTM-CRF for clinical concept extraction." arXiv preprint arXiv:1611.08373 (2016).
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
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)
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
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.
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.
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.
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.
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.
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).
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
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