Abstract
Accurate electronic health records are important for clinical care, research, and patient safety assurance. Correction of misspelled words is required to ensure the correct interpretation of medical records. In the Persian language, the lack of automated misspelling detection and correction system is evident in the medicine and health care. In this article, we describe the development of an automated misspelling detection and correction system for radiology and ultrasound’s free texts in the Persian language. To achieve our goal, we used n-gram language model and three different types of free texts related to abdominal and pelvic ultrasound, head and neck ultrasound, and breast ultrasound reports. Our system achieved the detection performance of up to 90.29% for radiology and ultrasound’s free texts with the correction accuracy of 88.56%. Results indicated that high-quality spelling correction is possible in clinical reports. The system also achieved significant savings during the documentation process and final approval of the reports in the imaging department.
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Abbreviations
- HIS:
-
Hospital information system
- EPR:
-
Electronic patient record
- EHR:
-
Electronic health record
- OCR:
-
Optical character recognition
References
Holzinger, A., et al., Biomedical text mining: state-of-the-art, open problems and future challenges, in Interactive knowledge discovery and data mining in biomedical informatics. 2014, Springer. p. 271–300.
Wong W, Glance D: Statistical semantic and clinician confidence analysis for correcting abbreviations and spelling errors in clinical progress notes. Artificial intelligence in medicine 53(3):171–180, 2011
Zhou L et al.: Analysis of errors in dictated clinical documents assisted by speech recognition software and professional transcriptionists. JAMA Network Open 1(3):e180530–e180530, 2018
Turchin, A., et al. Identification of misspelled words without a comprehensive dictionary using prevalence analysis. in AMIA Annual Symposium Proceedings. 2007. American Medical Informatics Association.
Dalianis, H., Clinical Text Mining: Secondary Use of Electronic Patient Records. 2018: Springer.
Dalianis, H., Clinical text retrieval-an overview of basic building blocks and applications, in Professional Search in the Modern World. 2014, Springer. p. 147–165.
Ringler MD, Goss BC, Bartholmai BJ: Syntactic and semantic errors in radiology reports associated with speech recognition software. Health informatics journal 23(1):3–13, 2017
Zech, J., et al., Detecting insertion, substitution, and deletion errors in radiology reports using neural sequence-to-sequence models. Annals of Translational Medicine, 2018.
Zhang, Y. Contextualizing consumer health information searching: an analysis of questions in a social Q&A community. in Proceedings of the 1st ACM International Health Informatics Symposium. 2010. ACM.
Golkar, A., et al. Improve word sense disambiguation by proposing a pruning method for optimizing conceptual density's contexts. in Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on. 2015. IEEE.
Sarker, A. and G. Gonzalez-Hernandez, An unsupervised and customizable misspelling generator for mining noisy health-related text sources. arXiv preprint arXiv:1806.00910, 2018.
Nizamuddin, U. and H. Dalianis. Detection of spelling errors in Swedish clinical text. in 1st Nordic workshop on evaluation of spellchecking and proofing tools (NorWEST2014), SLTC 2014, Uppsala. 2014.
Dalianis, H., Characteristics of Patient Records and Clinical Corpora, in Clinical Text Mining. 2018, Springer. p. 21–34.
Hussain, F. and U. Qamar. Identification and Correction of Misspelled Drugs Names in Electronic Medical Records (EMR). in ICEIS (2). 2016.
Kilicoglu H et al.: An ensemble method for spelling correction in consumer health questions. in AMIA Annual Symposium Proceedings. 2015. American Medical Informatics Association.
Zhou, X., et al., Context-sensitive spelling correction of consumer-generated content on health care. JMIR medical informatics, 2015. 3(3).
Ruch P, Baud R, Geissbühler A: Using lexical disambiguation and named-entity recognition to improve spelling correction in the electronic patient record. Artificial intelligence in medicine 29(1–2):169–184, 2003
Siklósi, B., A. Novák, and G. Prószéky. Context-aware correction of spelling errors in Hungarian medical documents. in International Conference on Statistical Language and Speech Processing. 2013. Springer.
Grigonyté, G., et al. Improving readability of Swedish electronic health records through lexical simplification: First results. in European Chapter of ACL (EACL), 26-30 April, 2014, Gothenburg, Sweden. 2014. Association for Computational Linguistics.
Tolentino HD et al.: A UMLS-based spell checker for natural language processing in vaccine safety. BMC medical informatics and decision making 7(1):3, 2007
Doan S, Bastarache L, Klimkowski S, Denny JC, Xu H: Integrating existing natural language processing tools for medication extraction from discharge summaries. Journal of the American Medical Informatics Association 17(5):528–531, 2010
Lai KH, Topaz M, Goss FR, Zhou L: Automated misspelling detection and correction in clinical free-text records. Journal of biomedical informatics 55:188–195, 2015
Fivez, P., S. Šuster, and W. Daelemans, Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings. arXiv preprint arXiv:1710.07045, 2017.
Pérez A, Atutxa A, Casillas A, Gojenola K, Sellart Á: Inferred joint multigram models for medical term normalization according to ICD. International journal of medical informatics 110:111–117, 2018
D’hondt, E., C. Grouin, and B. Grau. Low-resource OCR error detection and correction in French Clinical Texts. in Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis. 2016.
Faili H et al.: Vafa spell-checker for detecting spelling, grammatical, and real-word errors of Persian language. Literary and Linguistic Computing 31(1):95–117, 2014
Dowsett, D., Radiological sciences dictionary: keywords, names and definitions. 2009: CRC Press.
Damerau FJ: A technique for computer detection and correction of spelling errors. Communications of the ACM 7(3):171–176, 1964
Yazdani A, Safdari R, Golkar A, R Niakan Kalhori S: Words prediction based on N-gram model for free-text entry in electronic health records. Health information science and systems 7(1):6, 2019
Brown PF et al.: Class-based n-gram models of natural language. Computational linguistics 18(4):467–479, 1992
Minn MJ, Zandieh AR, Filice RW: Improving radiology report quality by rapidly notifying radiologist of report errors. Journal of digital imaging 28(4):492–498, 2015
Dashti SM: Real-word error correction with trigrams: correcting multiple errors in a sentence. Language Resources and Evaluation. 52(2):485–502, 2018 Jun 1
Kruskal JB, Reedy A, Pascal L, Rosen MP, Boiselle PM: Quality initiatives: lean approach to improving performance and efficiency in a radiology department. Radiographics. 32(2):573–587, 2012 Mar 5
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This study was funded by the Cancer Research Center of Cancer Institute of Iran/Sohrabi cancer charity (grant number 96-34375-51-01).
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Yazdani, A., Ghazisaeedi, M., Ahmadinejad, N. et al. Automated Misspelling Detection and Correction in Persian Clinical Text. J Digit Imaging 33, 555–562 (2020). https://doi.org/10.1007/s10278-019-00296-y
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DOI: https://doi.org/10.1007/s10278-019-00296-y