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Cansu Sen
Cansu Sen
CodaMetrix
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Human attention maps for text classification: Do humans and neural networks focus on the same words?
C Sen, T Hartvigsen, B Yin, X Kong, E Rundensteiner
Proceedings of the 58th annual meeting of the association for computational …, 2020
65*2020
Adverse drug event detection from electronic health records using hierarchical recurrent neural networks with dual-level embedding
S Wunnava, X Qin, T Kakar, C Sen, EA Rundensteiner, X Kong
Drug safety 42, 113-122, 2019
602019
Adaptive-halting policy network for early classification
T Hartvigsen, C Sen, X Kong, E Rundensteiner
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
472019
Time-aware transformer-based network for clinical notes series prediction
D Zhang, J Thadajarassiri, C Sen, E Rundensteiner
Machine learning for healthcare conference, 566-588, 2020
372020
Early Prediction of MRSA Infections using Electronic Health Records.
T Hartvigsen, C Sen, S Brownell, E Teeple, X Kong, EA Rundensteiner
HEALTHINF, 156-167, 2018
232018
Recurrent halting chain for early multi-label classification
T Hartvigsen, C Sen, X Kong, E Rundensteiner
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
222020
Crest-risk prediction for clostridium difficile infection using multimodal data mining
C Sen, T Hartvigsen, E Rundensteiner, K Claypool
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
152017
SVM-based sketch recognition: which hyperparameter interval to try?
KT Yesilbek11, C Sen11, S Cakmak11, TM Sezgin
92015
From extreme multi-label to multi-class: A hierarchical approach for automated ICD-10 coding using phrase-level attention
C Sen, B Ye, J Aslam, A Tahmasebi
arXiv preprint arXiv:2102.09136, 2021
62021
Patient-level classification on clinical note sequences guided by attributed hierarchical attention
C Sen, T Hartvigsen, X Kong, E Rundensteiner
2019 IEEE International Conference on Big Data (Big Data), 930-939, 2019
62019
Human-like explanation for text classification with limited attention supervision
D Zhang, C Sen, J Thadajarassiri, T Hartvigsen, X Kong, E Rundensteiner
2021 ieee international conference on big data (big data), 957-967, 2021
32021
Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection.
E Teeple, T Hartvigsen, C Sen, KT Claypool, EA Rundensteiner
HEALTHINF, 656-663, 2020
32020
Comparing general and locally-learned word embeddings for clinical text mining
J Thadajarassiri, C Sen, T Hartvigsen, X Kong, E Rundensteiner
2019 IEEE EMBS International Conference on Biomedical & Health Informatics …, 2019
32019
Learning Similarity-Preserving Meta-Embedding for Text Mining
J Thadajarassiri, C Sen, T Hartvigsen, X Kong, E Rundensteiner
2020 IEEE International Conference on Big Data (Big Data), 808-817, 2020
22020
Learning to Selectively Update State Neurons in Recurrent Networks
T Hartvigsen, C Sen, X Kong, E Rundensteiner
Proceedings of the 29th ACM International Conference on Information …, 2020
22020
Detecting MRSA infections by fusing structured and unstructured electronic health record data
T Hartvigsen, C Sen, EA Rundensteiner
Biomedical Engineering Systems and Technologies: 11th International Joint …, 2019
22019
SVM for sketch recognition: Which hyperparameter interval to try?
KT Yesilbek, C Sen, S Cakmak, TM Sezgin
2015 23nd Signal Processing and Communications Applications Conference (SIU …, 2015
12015
Attention-based Deep Learning Models for Text Classification and their Interpretability
C Sen
IBM Research, 2020
2020
Learning Temporal Relevance in Longitudinal Medical Notes
C Sen, T Hartvigsen, X Kong, E Rundensteiner
2019 IEEE International Conference on Big Data (Big Data), 2474-2483, 2019
2019
Reducing Computation in Recurrent Networks by Selectively Updating State Neurons
T Hartvigsen, C Sen, X Kong, E Rundensteiner
2019
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Artikelen 1–20