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The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature. [ABSTRACT FROM AUTHOR]

Aim To compare the oral health knowledge, attitude and behavior among pre-clinical and clinical undergraduate dental students. Methodology A questionnaire based cross-sectional study was conducted among pre-clinical and clinical undergraduate students at a teaching dental institution in Andhra Pradesh. A total of 357 students were selected using non- probability purposive sampling technique. Modified English version of Hiroshima University Dental Behavior Inventory (HU-DBI) Questionnaire consisting of 31 closed ended questions has been implemented in this study after performing appropriate psychometric analysis. Results Knowledge, attitude and behavior differences between preclinical and clinical students were analyzed by Student's t-testand statistically significant difference was obtained with p value of 0.004. Chi- square test was employed for dichotomous datai.e. to find the differences in the distribution of "yes" among students from the different academic year as well as between male and female students.Majority of females (86.3%) have responded that they don't use tooth brush with hard bristles for cleaning their teeth which is high when compared to males (73.4%) and the difference is statistically significant (p=0.01). There is also a statistically significant difference with respect to smoking habits between males and females (p=0.003). Summary and Conclusions The outcome of this study shows clinical students having higher oral health knowledge, attitude and behavior scoresthan preclinical students and also the self-reported quality of overall oral health behavior was more pronounced in clinical students. Specific educational and awarenessprograms should be planned for the preclinical students. [ABSTRACT FROM AUTHOR]

Background: Electronic health record (EHR) systems contain a large volume of texts, including visit notes, discharge summaries, and various reports. To protect the confidentiality of patients, these records often need to be fully de-identified before circulating for secondary use. Machine learning (ML) based named entity recognition (NER) model has emerged as a popular technique of automatic de-identification., Objective: The performance of a machine learning model highly depends on the selection of appropriate features. The objective of this study was to investigate the usability of multiple features in building a conditional random field (CRF) based clinical de-identification NER model., Methods: Using open-source natural language processing (NLP) toolkits, we annotated protected health information (PHI) in 1,500 pathology reports and built supervised NER models using multiple features and their combinations. We further investigated the dependency of a model's performance on the size of training data., Results: Among the 10 feature extractors explored in this study, n-gram, prefix-suffix, word embedding, and word shape performed the best. A model using combination of these four feature sets yielded precision, recall, and F1-score for each PHI as follows: NAME (0.80; 0.79; 0.80), LOCATION (0.85; 0.83; 0.84), DATE (0.86; 0.79; 0.82), HOSPITAL (0.96; 0.93; 0.95), ID (0.99; 0.82; 0.90), and INITIALS (0.97; 0.49; 0.65). We also found that the model's performance becomes saturated when the training data size is beyond 200., Conclusion: Manual de-identification of large-scale data is an impractical procedure since it is time-consuming and subject to human errors. Analysis of the NER model's performance in this study sheds light on a semi-automatic clinical de-identification pipeline for enterprise-wide data warehousing., Competing Interests: VS and MB were employed by F. Hoffmann-La Roche. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Paul, Rana, Tautam, Kotapati, Jampani, Singh, Islam, Mandhadi, Sharma, Barnes, Hammer and Mosa.)