Abstract
The incredible advances in machine learning have created a variety of predictive and decision-making medical models that greatly improve the efficacy of treatment and improve the quality of care. In healthcare, such models are often based on electronic health records (EHRs). The quality of this models depends on the quality of the EHRs, which are usually presented as plain unstructured text. Such records often contain spelling errors, which reduce the quality of intelligent systems based on them. In this paper we present a method and tool for correcting spelling errors in medical texts in Russian. By combining the Symmetrical Deletion algorithm and a finely tuned BERT model to correct spelling errors, the tool can improve the quality of original medical texts without significant cost. We have evaluated the correction precision and performance of the presented tool and compared it with other popular spelling error correction tools that support Russian language. Experiments have shown that the presented approach and tool are 7% superior to existing open-source tools for automatically correcting spelling errors in Russian medical texts. The proposed tool and its source code are available on GitHub\(^{1}\) and pip\(^{2}\) repositories(\(^1\)https://github.com/DmitryPogrebnoy/MedSpellChecker \(^{2}\)https://pypi.org/project/medspellchecker).
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Acknowledgments
This work was supported by the Ministry of Science and Higher Education of Russian Federation, goszadanie no. 2019-1339.
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Pogrebnoi, D., Funkner, A., Kovalchuk, S. (2023). RuMedSpellchecker: Correcting Spelling Errors for Natural Russian Language in Electronic Health Records Using Machine Learning Techniques. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10475. Springer, Cham. https://doi.org/10.1007/978-3-031-36024-4_16
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