Publications

2021

• Semi-Supervised Knowledge Amalgamation for Sequence Classification
  Jidapa Thadajarassiri, Thomas Hartvigsen, Xiangnan Kong, and Elke Rundensteiner.
  AAAI, 2021.
  [code]

2020

• Learning Similarity-Preserving Meta-Embedding for Text Mining
  Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, and Elke Rundensteiner.
  2020 IEEE International Conference on Big Data, accepted as regular paper.
  [code]
• Supervised Topic Compositional Neural Language Model for Clinical Narrative Understanding
  Xiao Qin, Cao Xiao, Tengfei Ma, Tabassum Kakar, Susmitha Wunnava, Xiangnan Kong, Elke Rundensteiner, and Fei Wang
  2020 IEEE International Conference on Big Data, accepted as regular paper.
• A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events
  Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke Rundensteiner and Xiangnan Kong.
  Findings of EMNLP, 2020.
  [video] [code]
• Learning to Selectively Update State Neurons in Recurrent Networks
  Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke Rundensteiner.
  CIKM, 2020. Research track.
  [pdf] [code]
• Time-Aware Transformer-based Network for Clinical Notes Series Prediction.
  Dongyu Zhang, Jidapa Thadajarassiri, Cansu Sen, Elke Rundensteiner.
  Machine Learning for Healthcare (MLHC), 2020.
  [pdf] [code]
• Recurrent Halting Chain for Early Multi-label Classification.
  Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke Rundensteiner.
  KDD, 2020. Research Track.
  [pdf] [code]
• Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?
  Cansu Sen, Thomas Hartvigsen, Biao Yin, Xiangnan Kong, Elke Rundensteiner.
  ACL, 2020. Long Paper.
  [pdf] [link] [data]
• Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection.
  Erin Teeple, Thomas Hartvigsen, Cansu Sen, Kajal Claypool, Elke Rundensteiner.
  HEALTHINF, 2020. Best Poster.
  [link]
• Designing a Visual Analytics System for Medication Error Screening and Detection.
  Tabassum Kakar, Xiao Qin, Cory Tapply, Oliver Spring, Derek Murphy, Daniel Yun, Elke Rundensteiner, Lane Harrison, Thang La, Sanjay K Sahoo.
  VISIGRAPP, 2020.

2019

• Adaptive-Halting Policy Network for Early Classification.
  Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke Rundensteiner.
  KDD, 2019. Research Track.
  [pdf] [link] [code]
• Adverse drug event detection from electronic health records using hierarchical recurrent neural networks with dual-level embedding.
  Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Cansu Sen, Elke Rundensteiner, Xiangnan Kong.
  Drug Safety, 42, 113-122, 2019.
• DIVA: Towards Validation of Hypothesized Drug-Drug Interactions via Visual Analysis.
  Tabassum Kakar, Xiao Qin, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo, Suranjan De.
  EuroVis, 2019.
• Patient-Level Classification of Clinical Note Sequences Guided by Attributed Hierarchical Attention.
  Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner.
  IEEE BigData, 2019
• Learning Temporal Relevance in Longitudinal Medical Notes.
  Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner.
  IEEE BigData, 2019.
• Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embeddings.
  Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Cansu Sen, Elke Rundensteiner, Xiangnan Kong.
  Journal of Drug Safety, 2019.
• Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining.
  Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner.
  IEEE BHI, 2019.
  [pdf]

2018

• One Size Does Not Fit All: An Ensemble Approach Towards Information Extraction from Adverse Drug Event Narratives.
  Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Xiangnan Kong, Elke A. Rundensteiner, Sanjay K. Sahoo, Suranjan De.
  HEALTHINF, 2018.
• Multi-layered Learning for Information Extraction from Adverse Drug Event Narratives.
  Susmitha Wunnava, Xiao Qin, Tabassum Kakar, M. L. Tlachac, Xiangnan Kong, Elke A. Rundensteiner, Sanjay K. Sahoo, Suranjan De.
  BIOSTEC, 2018.
• MeDIAR: Multi-Drug Adverse Reactions Analytics.
  Xiao Qin, Tabassum Kakar, Susmitha Wunnava, Brian McCarthy, Andrew Schade, Huy Quoc Tran, Brian Zylich, Elke Rundensteiner, Lane Harrison.
  ICDE, 2018. Demo paper.
• Deep Learning Strategies for the Automatic Detection of Medication and Adverse Drug Events from Electronic Health Records.
  Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Elke Rundensteiner, Xiangnan Kong.
  AMIA, 2018.
• DEVES: Interactive Signal Analytics for Drug Safety.
  Tabassum Kakar, Xiao Qin, Andrew Schade, Brian McCarthy, Huy Quoc Tran, Brian Zylich, Elke A. Rundensteiner, Lane Harrison, Sanjay K. Sahoo, Suranjan De.
  CIKM, 2018. Demo paper.
• Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data.
  Thomas Hartvigsen, Cansu Sen, Elke Rundensteiner.
  Communication in Computer and Information Science, Volume 1024, 2018.
  [link]
• Early Prediction of MRSA Infections using Electronic Health Records.
  Thomas Hartvigsen, Cansu Sen, Sarah Brownell, Erin Teeple, Xiangnan Kong, Elke Rundensteiner.
  HEALTHINF, 2018.
  [pdf]

2017

• MARAS: signaling multi-drug adverse reactions.
  Xiao Qin, Tabassum Kakar, Susmitha Wunnava, Elke A. Rundensteiner, Lei Cao.
  KDD, 2017. Applied Data Science Track.
• Towards transforming FDA adverse event narratives into actionable structured data for improved pharmacovigilance.
  Susmitha Wunnava, Xiao Qin, Tabassum Kakar, Vimig Socrates, Amber Wallace, Elke Rundensteiner.
  SAC, 2017.
• CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining.
  Cansu Sen, Thomas Hartvigsen, Kajal Claypool, Elke Rundensteiner.
  ECML, 2017.
  [link]