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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 15 of 15 1 GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records ... Yang, Xi; PourNejatian, Nima; Shin, Hoo Chang. - : arXiv, 2022 BASE Show details 2 Tracing Text Provenance via Context-Aware Lexical Substitution ... Yang, Xi; Zhang, Jie; Chen, Kejiang. - : arXiv, 2021 BASE Show details 3 Assessing mental health signals among sexual and gender minorities using Twitter data ... Yunpeng Zhao; Guo, Yi; He, Xing. - : Figshare, 2019 BASE Show details 4 Assessing mental health signals among sexual and gender minorities using Twitter data ... Yunpeng Zhao; Guo, Yi; He, Xing. - : Figshare, 2019 BASE Show details 5 A Study of Deep Learning Methods for De-identification of Clinical Notes at Cross Institute Settings Yang, Xi; Lyu, Tianchen; Lee, Chih-Yin. - 2019 BASE Show details 6 A study of deep learning methods for de-identification of clinical notes in cross-institute settings Yang, Xi; Lyu, Tianchen; Li, Qian; Lee, Chih-Yin; Bian, Jiang; Hogan, William R.; Wu, Yonghui. - : BioMed Central, 2019 Abstract: BACKGROUND: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems to de-identify clinical text at local hospitals. However, existing studies often utilized training and test data collected from the same institution. There are few studies to explore automated de-identification under cross-institute settings. The goal of this study is to examine deep learning-based de-identification methods at a cross-institute setting, identify the bottlenecks, and provide potential solutions. METHODS: We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based de-identification models using 2014 i2b2/UTHealth corpus, and evaluated the performance using UF corpus. We compared five different word embeddings trained from the general English text, clinical text, and biomedical literature, explored lexical and linguistic features, and compared two strategies to customize the deep learning models using UF notes and resources. RESULTS: Pre-trained word embeddings using a general English corpus achieved better performance than embeddings from de-identified clinical text and biomedical literature. The performance of deep learning models trained using only i2b2 corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8568 and 0.8958) when applied to another corpus annotated at UF Health. Linguistic features could further improve the performance of de-identification in cross-institute settings. After customizing the models using UF notes and resource, the best model achieved the strict and relaxed F1 scores of 0.9288 and 0.9584, respectively. CONCLUSIONS: It is necessary to customize de-identification models using local clinical text and other resources when applied in cross-institute settings. Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution. Keyword: Research URL: https://doi.org/10.1186/s12911-019-0935-4 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894104/ http://www.ncbi.nlm.nih.gov/pubmed/31801524 BASE Hide details 7 MADEx: A System for Detecting Medications, Adverse Drug Events, and their Relations from Clinical Notes Yang, Xi; Bian, Jiang; Gong, Yan. - 2019 BASE Show details 8 Rome Foundation-Asian working team report: Asian functional gastrointestinal disorder symptom clusters Siah, Kewin Tien Ho; Gong, Xiaorong; Yang, Xi Jessie. - : BMJ Publishing Group Ltd, 2018 BASE Show details 9 Assessing Mental Health Signals among Sexual and Gender Minorities using Twitter Data Zhao, Yunpeng; Guo, Yi; He, Xing. - 2018 BASE Show details 10 An Analytical Study of Not-negation and No-negation Translated in the Chinese Version of the Fantasy Fiction The Hobbit Yang, Xi. - : The University of Queensland, School of Languages and Cultures, 2018 BASE Show details 11 Potential screening and early diagnosis method for cancer: Tongue diagnosis HAN, SHUWEN; YANG, XI; QI, QUAN. - : D.A. Spandidos, 2016 BASE Show details 12 Mirror neuron system based therapy for aphasia rehabilitation Chen, Wenli; Ye, Qian; Ji, Xiangtong. - : Frontiers Media S.A., 2015 BASE Show details 13 A shape-initialized and intensity-adaptive level set method for auroral oval segmentation Yang, Xi; Gao, Xinbo; Li, Jie... In: Information sciences. - New York, NY : Elsevier Science Inc. 277 (2014), 794-807 OLC Linguistik Show details 14 Discriminatively trained GMMs for language classification using boosting methods Yang, Xi; Siu, Man-Hung; Gish, Herbert In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 17 (2009) 1, 187-197 BLLDB OLC Linguistik Show details 15 Research On The Implications Of Business English Teaching On Bilingual Courses In Business Communication. Yang Xi BASE Show details

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