Automated Misspelling Detection and Correction in Persian Clinical Text

Automated Misspelling Detection and Correction in Persian Clinical Text Publisher Pubmed

Yazdani A^{1, 2} ; Ghazisaeedi M³ ; Ahmadinejad N^{4, 5} ; Giti M^{5, 6} ; Amjadi H⁷ ; Nahvijou A⁸

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1. Department of Health Information Technology, School of Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
2. Health Human Resources Research Center, School of Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
3. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences(TUMS), Tehran, Iran
4. Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences (TUMS), Tehran, Iran
5. Medical Imaging Center, Cancer Research Institute, Imam Khomeini Hospital, Tehran, Iran
6. Breast Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences (TUMS), Tehran, Iran
7. Human Resource Department, Imam Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran
8. Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences(TUMS), Tehran, Iran

Source: Journal of Digital Imaging Published:2020

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. © 2019, Society for Imaging Informatics in Medicine.

Style	Citing Format
MLA	Yazdani A, et al.. "Automated Misspelling Detection and Correction in Persian Clinical Text." Journal of Digital Imaging, vol. 33, no. 3, 2020, pp. 555-562.
APA	Yazdani A, Ghazisaeedi M, Ahmadinejad N, Giti M, Amjadi H, Nahvijou A (2020). Automated Misspelling Detection and Correction in Persian Clinical Text. Journal of Digital Imaging, 33(3), 555-562.
Chicago	Yazdani A, Ghazisaeedi M, Ahmadinejad N, Giti M, Amjadi H, Nahvijou A. "Automated Misspelling Detection and Correction in Persian Clinical Text." Journal of Digital Imaging 33, no. 3 (2020): 555-562.
Harvard	Yazdani A et al. (2020) 'Automated Misspelling Detection and Correction in Persian Clinical Text', Journal of Digital Imaging, 33(3), pp. 555-562.
Vancouver	Yazdani A, Ghazisaeedi M, Ahmadinejad N, Giti M, Amjadi H, Nahvijou A. Automated Misspelling Detection and Correction in Persian Clinical Text. Journal of Digital Imaging. 2020;33(3):555-562.
BibTex	@article{ author = {Yazdani A and Ghazisaeedi M and Ahmadinejad N and Giti M and Amjadi H and Nahvijou A}, title = {Automated Misspelling Detection and Correction in Persian Clinical Text}, journal = {Journal of Digital Imaging}, volume = {33}, number = {3}, pages = {555-562}, year = {2020} }
RIS	TY - JOUR AU - Yazdani A AU - Ghazisaeedi M AU - Ahmadinejad N AU - Giti M AU - Amjadi H AU - Nahvijou A TI - Automated Misspelling Detection and Correction in Persian Clinical Text JO - Journal of Digital Imaging VL - 33 IS - 3 SP - 555 EP - 562 PY - 2020 ER -

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