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A context-aware approach for progression tracking of medical concepts in electronic medical records.

, , , , , and . J. Biomed. Informatics, (2015)

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HTNSystem: Hypertension Information Extraction System for Unstructured Clinical Notes., , , , and . TAAI, volume 8916 of Lecture Notes in Computer Science, page 219-227. Springer, (2014)Feature Engineering for Recognizing Adverse Drug Reactions from Twitter Posts., , , and . Information, 7 (2): 27 (2016)TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7., , , , and . SemEval@COLING, page 663-667. The Association for Computer Linguistics, (2014)TMUNSW: Identification of Disorders and Normalization to SNOMED-CT Terminology in Unstructured Clinical Notes., , , , and . SemEval@NAACL-HLT, page 394-398. The Association for Computer Linguistics, (2015)A preliminary study on automatic identification of patient smoking status in unstructured electronic health records., , , and . BioNLP@IJCNLP, page 147-151. Association for Computational Linguistics, (2015)Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging., , , , and . MICCAI (5), volume 12265 of Lecture Notes in Computer Science, page 529-539. Springer, (2020)Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature., , , , , , and . Database J. Biol. Databases Curation, (2019)Colorectal Cancer Survival Prediction Using Deep Distribution Based Multiple-Instance Learning., , , , and . Entropy, 24 (11): 1669 (November 2022)DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture., , , , , and . CoRR, (2023)Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA with Less Data using Swin Transformer., , , and . CoRR, (2022)