Seyed Mohammad Sadegh Dashti, Seyedeh Fatemeh Dashti
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This model is complemented by an innovative orthographic similarity matching algorithm, PERTO, which uses visual similarity of characters for ranking correction candidates.</p><p><strong>Results: </strong>The evaluation of our approach demonstrated its robustness and precision in detecting and rectifying word errors in Persian clinical text. In terms of non-word error correction, our model achieved an F1-Score of 90.0% when the PERTO algorithm was employed. For real-word error detection, our model demonstrated its highest performance, achieving an F1-Score of 90.6%. Furthermore, the model reached its highest F1-Score of 91.5% for real-word error correction when the PERTO algorithm was employed.</p><p><strong>Conclusions: </strong>Despite certain limitations, our method represents a substantial advancement in the field of spelling error detection and correction for Persian clinical text. By effectively addressing the unique challenges posed by the Persian language, our approach paves the way for more accurate and efficient clinical documentation, contributing to improved patient care and safety. Future research could explore its use in other areas of the Persian medical domain, enhancing its impact and utility.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299402/pdf/","citationCount":"0","resultStr":"{\"title\":\"Improving the quality of Persian clinical text with a novel spelling correction system.\",\"authors\":\"Seyed Mohammad Sadegh Dashti, Seyedeh Fatemeh Dashti\",\"doi\":\"10.1186/s12911-024-02613-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The accuracy of spelling in Electronic Health Records (EHRs) is a critical factor for efficient clinical care, research, and ensuring patient safety. 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引用次数: 0
摘要
背景:电子健康记录(EHR)中拼写的准确性是高效临床护理、研究和确保患者安全的关键因素。波斯语词汇丰富、特点复杂,给实词纠错带来了独特的挑战。本研究旨在开发一种创新方法,用于检测和纠正波斯语临床文本中的拼写错误:我们的策略采用了最先进的预训练模型,该模型专门针对波斯语临床领域的拼写纠正任务进行了细致的微调。该模型还辅以创新的正字法相似性匹配算法 PERTO,该算法利用字符的视觉相似性对候选更正进行排序:结果:对我们的方法进行的评估表明,该方法在检测和纠正波斯语临床文本中的单词错误方面具有稳健性和精确性。在非单词纠错方面,当使用 PERTO 算法时,我们的模型达到了 90.0% 的 F1 分数。在实词错误检测方面,我们的模型表现出了最高的性能,F1 分数达到了 90.6%。此外,在使用 PERTO 算法进行实词纠错时,该模型的 F1 分数也达到了最高的 91.5%:尽管存在一定的局限性,但我们的方法代表了波斯语临床文本拼写错误检测和纠正领域的一大进步。通过有效解决波斯语所带来的独特挑战,我们的方法为更准确、更高效的临床记录铺平了道路,有助于改善患者护理和安全性。未来的研究可以探索其在波斯语医疗领域其他方面的应用,从而增强其影响力和实用性。
Improving the quality of Persian clinical text with a novel spelling correction system.
Background: The accuracy of spelling in Electronic Health Records (EHRs) is a critical factor for efficient clinical care, research, and ensuring patient safety. The Persian language, with its abundant vocabulary and complex characteristics, poses unique challenges for real-word error correction. This research aimed to develop an innovative approach for detecting and correcting spelling errors in Persian clinical text.
Methods: Our strategy employs a state-of-the-art pre-trained model that has been meticulously fine-tuned specifically for the task of spelling correction in the Persian clinical domain. This model is complemented by an innovative orthographic similarity matching algorithm, PERTO, which uses visual similarity of characters for ranking correction candidates.
Results: The evaluation of our approach demonstrated its robustness and precision in detecting and rectifying word errors in Persian clinical text. In terms of non-word error correction, our model achieved an F1-Score of 90.0% when the PERTO algorithm was employed. For real-word error detection, our model demonstrated its highest performance, achieving an F1-Score of 90.6%. Furthermore, the model reached its highest F1-Score of 91.5% for real-word error correction when the PERTO algorithm was employed.
Conclusions: Despite certain limitations, our method represents a substantial advancement in the field of spelling error detection and correction for Persian clinical text. By effectively addressing the unique challenges posed by the Persian language, our approach paves the way for more accurate and efficient clinical documentation, contributing to improved patient care and safety. Future research could explore its use in other areas of the Persian medical domain, enhancing its impact and utility.
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.