1. Trang chủ
  2. » Luận Văn - Báo Cáo

Rút trích quan hệ về thời gian từ bệnh án điện tử

62 9 0

Đang tải... (xem toàn văn)

Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống

THÔNG TIN TÀI LIỆU

Thông tin cơ bản

Định dạng
Số trang 62
Dung lượng 541,62 KB

Nội dung

Ngày đăng: 26/01/2021, 21:16

Nguồn tham khảo

Tài liệu tham khảo Loại Chi tiết
[2] Chang Y C, Wu JC Y, Chen J M, et al. TEMPTing System: A Hybrid Method of Rule and Machine Learning for Temporal Relation Extraction in Patient Discharge Summaries. Journal of Biomedical Informatics 2013 (in press) Sách, tạp chí
Tiêu đề: Journal of Biomedical Informatics 2013
[3] CRF++: Yet Another CRF toolkit, http://taku910.github.io/crfpp/,2013 [4] Dinh, D., Hoang, K., & Nguyen, T. V. (2001). Vietnamese WordSegmentation. In NLPRS (Vol. 1, pp. 749-756) Sách, tạp chí
Tiêu đề: http://taku910.github.io/crfpp/,2013" [4] Dinh, D., Hoang, K., & Nguyen, T. V. (2001). Vietnamese Word Segmentation. In "NLPRS
Tác giả: CRF++: Yet Another CRF toolkit, http://taku910.github.io/crfpp/,2013 [4] Dinh, D., Hoang, K., & Nguyen, T. V
Năm: 2001
[5] Dinh, D.,& Vu, T. (2006). A maximum entropy approach for Vietnamese word segmentation. Proceeding of 4 th IEEE international conference on Computer Science – Research, Innovation and Vision of the future 2006 (RIVF’06). Ho Chi Minh City, Vietnam, 247-252 Sách, tạp chí
Tiêu đề: Proceeding of 4"th" IEEE international conference on Computer Science – Research, Innovation and Vision of the future 2006 (RIVF’06)
Tác giả: Dinh, D.,& Vu, T
Năm: 2006
[7] Holmes AB, Hawson A, Liu F, Friedman C, Khiabanian H, Rabadan R. Discovering disease associations by integrating electronic clinical data and medical literature. PLoS One 2011, 6(6):e21132 Sách, tạp chí
Tiêu đề: PLoS One
[8] Jiang M, Chen Y, Liu M, et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inform Assoc (2011). doi:10.1136/amiajnl-2011- 000163 Sách, tạp chí
Tiêu đề: J Am Med Inform Assoc
Tác giả: Jiang M, Chen Y, Liu M, et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inform Assoc
Năm: 2011
[9] Joachims T. Text categorization with support vector machines: Learning with many relevant features (1998). Springer Berlin Heidelberg. (pp. 137-142) Sách, tạp chí
Tiêu đề: Springer Berlin Heidelberg
Tác giả: Joachims T. Text categorization with support vector machines: Learning with many relevant features
Năm: 1998
[13] Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken) 2010;62:1120e7 Sách, tạp chí
Tiêu đề: Arthritis Care Res
[14] Pustejovsky J, Hanks P, Sauri R, et al. The timebank corpus. Proceeding of Corpus Linguistics, Lancaster University, UK, 2003, 647–56 Sách, tạp chí
Tiêu đề: Proceeding of Corpus Linguistics, Lancaster University, UK
[15] Pustejovsky J, Verhagen M. SemEval-2010 task 13: evaluating events, time expressions, and temporal relations (TempEval-2). Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Boulder, Colorado: Association for Computational Linguistics, 2009: 112–16 Sách, tạp chí
Tiêu đề: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions. Boulder, Colorado: Association for Computational Linguistics
[16] Rong-En F , Kai-Wei C , Cho-Jui H , Xiang-Rui W , Chih-Jen L. LIBLINEAR: A Library for Large Linear Classification. The Journal of Machine Learning Research, 9, p.1871-1874, 6/1/2008 Sách, tạp chí
Tiêu đề: The Journal of Machine Learning Research
[19] Sohn S, Wagholikar K, Li D, et al. Comprehensive temporal information discovery from discharge summaries: medical events, time, and TLINK identification. JAm Med Inform Assoc. 2013 (in press) Sách, tạp chí
Tiêu đề: JAm Med Inform Assoc
[20] Stanford Parser, http://nlp.stanford.edu/software/lex-parser.shtml#Download [21] Strửtgen J, Armiti A, Van Canh T, Zell J, and Gertz M. HeidelTime: Highquality rule-based extraction and normalization of temporal expressions. In Proceedings of the 5th International Workshop on Semantic Evaluation.321–324 Sách, tạp chí
Tiêu đề: Proceedings of the 5th International Workshop on Semantic Evaluation
[23] Sun W, Rumshisky A, Uzuner O. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge. J Am Med Inform Assoc 2013;20:5 806-813 [24] Tang B, Wu Y, Jiang M, et al. A hybrid system for temporal informationextraction from clinical text. J Am Med Inform Assoc 2013 (in press) Sách, tạp chí
Tiêu đề: J Am Med Inform Assoc" 2013;20:5 806-813 [24] Tang B, Wu Y, Jiang M, et al. A hybrid system for temporal information extraction from clinical text. "J Am Med Inform Assoc
[26] Wu J, Roy J, Stewart WF. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med Care 2010; 48(6 Suppl): S106–S113 Sách, tạp chí
Tiêu đề: Med Care
[27] Xu H, Fu Z, Shah A, et al. Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. AMIA. Annu.Symp. Proc. 2011;2011:1564-1572 Sách, tạp chí
Tiêu đề: AMIA. Annu. "Symp
[28] Xu Y, et al. An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge. J Am Med Inform Assoc 2013;0:1–10.doi:10.1136/amiajnl-2012-001607 Sách, tạp chí
Tiêu đề: J Am Med Inform Assoc
[29] Wallach, H.M. Conditional random fields: An introduction. Technical Reports (CIS), 2014 Sách, tạp chí
Tiêu đề: Technical Reports (CIS)
[30] Zhang R, Cairelli M J, Fiszman M, et al. Using semantic predications to uncover drug—drug interactions in clinical data. Journal of Biomedical Informatics, 2014 Sách, tạp chí
Tiêu đề: Journal of Biomedical Informatics
[6] JVnTextPro: A Java-based Vietnamese Text Processing Tool, http://jvntextpro.sourceforge.net/ Link
[25] TreeTagger - a language independent part-of-speech tagger http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Link

TỪ KHÓA LIÊN QUAN

TÀI LIỆU CÙNG NGƯỜI DÙNG

TÀI LIỆU LIÊN QUAN

w