BibBase: http://fenway.cs.uml.edu/papers/pubs-all.bib
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  2020 (6)
Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation. Rongali, S.; Rose, A. J.; McManus, D. D.; Bajracharya, A. S.; Kapoor, A.; Granillo, E.; and Yu, H. Journal of Medical Internet Research, 22(3): e16374. 2020. Company: Journal of Medical Internet Research Distributor: Journal of Medical Internet Research Institution: Journal of Medical Internet Research Label: Journal of Medical Internet Research Publisher: JMIR Publications Inc., Toronto, Canada
Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation [link]Paper   doi   bibtex   abstract
A Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation. Li, R.; Wang, X.; and Yu, H. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, 2020.
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Calibrating Structured Output Predictors for Natural Language Processing. Jagannatha, A.; and Yu, H. In 2020 Annual Conference of the Association for Computational Linguistics (ACL), 2020.
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BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab. Jin, Y.; Li, F.; and Yu, H. In 2020 Annual Conference of the Association for Computational Linguistics (ACL), 2020.
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ICD Coding from Clinical Text Using Multi-­‐Filter Residual Convolutional Neural Network. T. Li; and Yu, H. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020.
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Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation. Hu, B.; Bajracharya, A.; and Yu, H. JMIR Medical Informatics, 8(1): e14971. 2020. Company: JMIR Medical Informatics Distributor: JMIR Medical Informatics Institution: JMIR Medical Informatics Label: JMIR Medical Informatics Publisher: JMIR Publications Inc., Toronto, Canada
Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation [link]Paper   doi   bibtex   abstract
  2019 (18)
Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance. Chen, J.; Lalor, J.; Liu, W.; Druhl, E.; Granillo, E.; Vimalananda, V. G; and Yu, H. Journal of Medical Internet Research, 21(3). March 2019.
Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance [link]Paper   doi   bibtex   abstract
Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study. Jin, Y.; Li, F.; Vimalananda, V. G.; and Yu, H. JMIR Medical Informatics, 7(4): e14340. 2019.
Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study [link]Paper   doi   bibtex   abstract
Learning to detect and understand drug discontinuation events from clinical narratives. Liu, F.; Pradhan, R.; Druhl, E.; Freund, E.; Liu, W.; Sauer, B. C.; Cunningham, F.; Gordon, A. J.; Peters, C. B.; and Yu, H. Journal of the American Medical Informatics Association, 26(10): 943–951. October 2019.
Learning to detect and understand drug discontinuation events from clinical narratives [link]Paper   doi   bibtex   abstract
Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0). Jagannatha, A.; Liu, F.; Liu, W.; and Yu, H. Drug Safety, (1): 99–111. January 2019.
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Naranjo Question Answering using End-to-End Multi-task Learning Model. Rawat, B. P; Li, F.; and Yu, H. 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD),2547–2555. 2019.
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A neural abstractive summarization model guided with topic sentences. ICONIP. Chen, C.; Hu, B.; Chen, Q.; and Yu, H. In 2019.
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An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models. Li, F.; and Yu, H. Journal of the American Medical Informatics Association, 26(7): 646–654. July 2019.
An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models [link]Paper   doi   bibtex   abstract
Anticoagulant prescribing for non-valvular atrial fibrillation in the Veterans Health Administration. Rose, A.; Goldberg, R; McManus, D.; Kapoor, A; Wang, V; Liu, W; and Yu, H Journal of the American Heart Association. 2019.
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Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. Lalor, J. P.; Wu, H.; and Yu, H. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4240–4250, Hong Kong, China, November 2019. Association for Computational Linguistics NIHMSID: NIHMS1059054
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds [link]Paper   doi   bibtex   abstract
Clinical Question Answering from Electronic Health Records. In the MLHC 2019 research track proceedings. Singh, B.; Li, F.; and Yu, H. In The MLHC 2019 research track proceedings, 2019.
Clinical Question Answering from Electronic Health Records. In the MLHC 2019 research track proceedings [pdf]Paper   bibtex
Comparing Human and DNN-Ensemble Response Patterns for Item Response Theory Model Fitting. Lalor, J.; Wu, H.; and Yu, H. 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)The Workshop on Cognitive Modeling and Computational Linguistics (CMCL). 2019.
Comparing Human and DNN-Ensemble Response Patterns for Item Response Theory Model Fitting [pdf]Paper   bibtex
QuikLitE, a Framework for Quick Literacy Evaluation in Medicine: Development and Validation. Zheng, J.; and Yu, H. Journal of Medical Internet Research, 21(2): e12525. 2019.
QuikLitE, a Framework for Quick Literacy Evaluation in Medicine: Development and Validation [link]Paper   doi   bibtex   abstract
Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records. Liu, F.; Jagannatha, A.; and Yu, H. Drug Safety. January 2019.
Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records [link]Paper   doi   bibtex
Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers. Lalor, J. P.; Woolf, B.; and Yu, H. Journal of Medical Internet Research, 21(1): e10793. 2019.
Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers [link]Paper   doi   bibtex   abstract
Generating Classical Chinese Poems from Vernacular Chinese. Yang, Z.; Cai, P.; Feng, Y.; Li, F.; Feng, W.; Chiu, E. S.; and yu , h. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6156–6165, Hong Kong, China, November 2019. Association for Computational Linguistics
Generating Classical Chinese Poems from Vernacular Chinese [link]Paper   doi   bibtex   abstract
Method for Meta-Level Continual Learning. Yu, H.; and Munkhdalai, T. January 2019.
Method for Meta-Level Continual Learning [link]Paper   bibtex   abstract
Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning. Liu, F.; Weng, C.; and Yu, H. In Richesson, R. L.; and Andrews, J. E., editor(s), Clinical Research Informatics, of Health Informatics, pages 357–378. Springer International Publishing, Cham, 2019.
Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning [link]Paper   doi   bibtex   abstract
Automatic extraction of quantitative data from ClinicalTrials.gov to conduct meta-analyses. Pradhan, R.; Hoaglin, D. C.; Cornell, M.; Liu, W.; Wang, V.; and Yu, H. Journal of Clinical Epidemiology, 105: 92–100. January 2019.
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  2018 (19)
Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning. Munkhdalai, T.; Liu, F.; and Yu, H. JMIR public health and surveillance, 4(2): e29. April 2018.
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A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews. Chen, J.; Druhl, E.; Polepalli Ramesh, B.; Houston, T. K.; Brandt, C. A.; Zulman, D. M.; Vimalananda, V. G.; Malkani, S.; and Yu, H. Journal of Medical Internet Research, 20(1): e26. January 2018.
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A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes. Rumeng, L.; Abhyuday N, J.; and Hong, Y. AMIA Annual Symposium Proceedings, 2017: 1149–1158. April 2018.
A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes [link]Paper   bibtex   abstract
Assessing the Readability of Medical Documents: A Ranking Approach. Zheng, J.; and Yu, H. JMIR medical informatics, 6(1): e17. March 2018.
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Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study. Lalor, J.; Wu, H.; Munkhdalai, T.; and Yu, H. In EMNLP, 2018.
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study [link]Paper   doi   bibtex   abstract
Soft Label Memorization-Generalization for Natural Language Inference. Lalor, J.; Wu, H.; and Yu, H. In 2018.
Soft Label Memorization-Generalization for Natural Language Inference. [link]Paper   bibtex   abstract
Sentence Simplification with Memory-Augmented Neural Networks. Vu, T.; Hu, B.; Munkhdalai, T.; and Yu, H. In North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018.
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Recent Trends In Oral Anticoagulant Use and Post-Discharge Complications Among Atrial Fibrillation Patients With Acute Myocardial Infarction. Amartya Kundu; Kevin O ’Day; Darleen M. Lessard; Joel M. Gore1; Steven A. Lubitz; Hong Yu; Mohammed W. Akhter; Daniel Z. Fisher; Robert M. Hayward Jr.; Nils Henninger; Jane S. Saczynski; Allan J. Walkey; Alok Kapoor; Jorge Yarzebski; Robert J. Goldberg; and David D. McManus In 2018. Journal of Atrial Fibrillation
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ComprehENotes: An Instrument to Assess Patient EHR Note Reading Comprehension of Electronic Health Record Notes: Development and Validation. Lalor, J; Wu, H; Chen, L; Mazor, K; and Yu, H The Journal of Medical Internet Research. April 2018.
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Detecting Hypoglycemia Incidence from Patients’ Secure Messages. Chen, J; and Yu, H In 2018.
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Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning. Li, F.; Liu, W.; and Yu, H. JMIR medical informatics, 6(4): e12159. November 2018.
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Reference Standard Development to Train Natural Language Processing Algorithms to Detect Problematic Buprenorphine-Naloxone Therapy. Celena B Peters; Fran Cunningham; Adam Gordon; Hong Yu; Cedric Salone; Jessica Zacher; Ronald Carico; Jianwei Leng; Nikolh Durley; Weisong Liu; Chao-Chin Lu; Emily Druhl; Feifan Liu; and Brian C Sauer In VA Pharmacy Informatics Conference 2018, 2018.
Reference Standard Development to Train Natural Language Processing Algorithms to Detect Problematic Buprenorphine-Naloxone Therapy [link]Paper   bibtex
Inadequate diversity of information resources searched in US-affiliated systematic reviews and meta-analyses: 2005-2016. Pradhan, R.; Garnick, K.; Barkondaj, B.; Jordan, H. S.; Ash, A.; and Yu, H. Journal of Clinical Epidemiology, 102: 50–62. October 2018.
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ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation. Lalor, J. P.; Wu, H.; Chen, L.; Mazor, K. M.; and Yu, H. Journal of Medical Internet Research, 20(4): e139. April 2018.
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Recent Trends in Oral Anticoagulant Use and Post-Discharge Complications Among Atrial Fibrillation Patients with Acute Myocardial Infarction. Kundu, A.; Day, K. O.; Lessard, D. M.; Gore, J. M.; Lubitz, S. A.; Yu, H.; Akhter, M. W.; Fisher, D. Z.; Hayward, R. M.; Henninger, N.; Saczynski, J. S.; Walkey, A. J.; Kapoor, A.; Yarzebski, J.; Goldberg, R. J.; and McManus, D. D. Journal of Atrial Fibrillation, 10(5): 1749. February 2018.
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Accuracy of International Classification of Disease Clinical Modification Codes for Detecting Bleeding Events in Electronic Health Records and When to Use Them. Wang, V; McManus, D; Ash, A; Hoaglin, D; and Yu, H In 2018.
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Frontiers of Clinical Information Extraction: Current Progress in Medication and Adverse Drug Event Detection from Electronic Health Records. Abhyuday Jagannatha; Feifan Liu; Weisong Liu; and Hong Yu In 9th Annual Pharmacy Informatics Conference, 2018.
Frontiers of Clinical Information Extraction: Current Progress in Medication and Adverse Drug Event Detection from Electronic Health Records [link]Paper   bibtex
Panel – Deep Learning for Healthcare - Hype or the Real Thing?. J. Sun; B. Westover; H. Yu; D. Sontag; and M. Ghassemi In AMIA 2018 Informatics Summit, 2018.
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Panel - Frontiers of Clinical Information Extraction: Current Progress in Medication and Adverse Drug Event Detection from Electronic Health Records. Feifan Liu; Abhyuday Jagannatha; and Hong Yu In AMIA 2018 Informatics Summit, 2018.
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  2017 (13)
Meta Networks. Munkhdalai, T.; and Yu, H. In ICML, volume 70, pages 2554–2563, Sydney, Australia, August 2017.
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Neural Semantic Encoders. Munkhdalai, T; and Yu, H. In European Chapter of the Association for Computational Linguistics 2017 (EACL), volume 1, pages 397–407, April 2017.
Neural Semantic Encoders [pdf]Paper   bibtex   abstract
Detecting Opioid-Related Aberrant Behavior using Natural Language Processing. Lingeman, J. M.; Wang, P.; Becker, W.; and Yu, H. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017: 1179–1185. 2017.
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CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability. Lalor, J; Wu, H; and Yu, H In February 2017.
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Assessing Electronic Health Record Readability. Zheng, J; and Yu, H In 2017.
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Reasoning with memory augmented neural networks for language comprehension. Munkhdalai, T.; and Yu, H. 5th International Conference on Learning Representations (ICLR). 2017.
Reasoning with memory augmented neural networks for language comprehension. [link]Paper   bibtex   abstract
Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study. Zheng, J.; and Yu, H. Journal of Medical Internet Research, 19(3): e59. 2017.
Readability Formulas and User Perceptions of Electronic Health Records Difficulty: A Corpus Study [link]Paper   doi   bibtex   abstract
Neural Tree Indexers for Text Understanding. Munkhdalai, T.; and Yu, H. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 11–21, Valencia, Spain, April 2017. Association for Computational Linguistics
Neural Tree Indexers for Text Understanding [link]Paper   bibtex   abstract
Generating a Test of Electronic Health Record Narrative Comprehension with Item Response Theory. Lalor, J; Wu, H; Chen, L; Mazor, K; and Yu, H In November 2017.
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An Analysis of Ability in Deep Neural Networks. Lalor, J. P.; Wu, H.; Munkhdalai, T.; and Yu, H. arXiv preprint arXiv:1702.04811. 2017.
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Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach. Chen, J.; Jagannatha, A. N.; Fodeh, S. J.; and Yu, H. JMIR medical informatics, 5(4): e42. October 2017.
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Improving Machine Learning Ability with Fine-Tuning. Lalor, J.; Wu, H.; and Yu, H. In ICML, 2017.
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An Analysis of Machine Learning Intelligence. Lalor, J. P.; Wu, H.; Munkhdalai, T.; and Yu, H. arXiv:1702.04811 [cs]. February 2017. arXiv: 1702.04811
An Analysis of Machine Learning Intelligence [link]Paper   bibtex   abstract
  2016 (11)
Structured prediction models for RNN based sequence labeling in clinical text. Jagannatha, A. N.; and Yu, H. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, volume 2016, pages 856–865, November 2016.
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RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. Choi, E.; Bahadori, M. T.; Sun, J.; Kulas, J.; Schuetz, A.; and Stewart, W. In Advances in Neural Information Processing Systems, pages 3504–3512, 2016.
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism [link]Paper   bibtex
Learning to Rank Scientific Documents from the Crowd. Lingeman, J. M; and Yu, H. arXiv:1611.01400. November 2016.
Learning to Rank Scientific Documents from the Crowd [pdf]Paper   bibtex   abstract
Learning for Biomedical Information Extraction: Methodological Review of Recent Advances. Liu, F.; Chen, J.; Jagannatha, A.; and Yu, H. arXiv:1606.07993. June 2016.
Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [pdf]Paper   bibtex   abstract
Citation Analysis with Neural Attention Models. Munkhdalai, M; Lalor, J; and Yu, H In Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis (LOUHI) ,, pages 69–77, Austin, TX, November 2016. Association for Computational Linguistics
Citation Analysis with Neural Attention Models [pdf]Paper   doi   bibtex
Condensed Memory Networks for Clinical Diagnostic Inferencing. Prakash, A.; Zhao, S.; Hasan, S. A.; Datla, V.; Lee, K.; Qadir, A.; Liu, J.; and Farri, O. arXiv:1612.01848 [cs]. December 2016. arXiv: 1612.01848
Condensed Memory Networks for Clinical Diagnostic Inferencing [link]Paper   bibtex   abstract
Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations. Chen, J.; Zheng, J.; and Yu, H. JMIR medical informatics, 4(4): e40. November 2016.
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EHR Note Paraphrasing for NoteAid Evaluation. Yu, H. In SBM, 2016.
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Mismatch between Patient Information-Seeking and Physician Expectation at a Diabetes Outpatient Clinic. Yu, H.; Makkapati, S.; Maranda, L.; and Malkani, S. In SBM, 2016.
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Building an Evaluation Scale using Item Response Theory. Lalor, J. P.; Wu, H.; and Yu, H. arXiv:1605.08889 [cs]. May 2016. arXiv: 1605.08889
Building an Evaluation Scale using Item Response Theory [link]Paper   bibtex   abstract
Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records. Jagannatha, A.; and Yu, H. arXiv:1606.07953 [cs]. June 2016. arXiv: 1606.07953
Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records [link]Paper   bibtex   abstract
  2015 (9)
Translating Electronic Health Record Notes from English to Spanish: A Preliminary Study. Liu, W.; Cai, S.; Balaji, R.; Chiriboga, G.; Knight, K.; and Yu, H. In ACL-IJCNLP, pages 134, Bei Jing, China, July 2015.
Translating Electronic Health Record Notes from English to Spanish: A Preliminary Study [pdf]Paper   doi   bibtex
Figure-Associated Text Summarization and Evaluation. Polepalli Ramesh, B.; Sethi, R. J.; and Yu, H. PLOS ONE, 10(2): e0115671. February 2015.
Figure-Associated Text Summarization and Evaluation [link]Paper   doi   bibtex
DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures. Yin, X.; Yang, C.; Pei, W.; Man, H.; Zhang, J.; Learned-Miller, E.; and Yu, H. PLoS ONE, 10(5). May 2015.
DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures [link]Paper   doi   bibtex   abstract
Methods for Linking EHR Notes to Education Materials. Zheng, J.; and Yu, H. AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science, 2015: 209–215. 2015.
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Identifying Key Concepts from EHR Notes Using Domain Adaptation. Zheng, J.; Yu, H.; and Bedford, M. A. In SIXTH INTERNATIONAL WORKSHOP ON HEALTH TEXT MINING AND INFORMATION ANALYSIS (LOUHI), pages 115, 2015.
Identifying Key Concepts from EHR Notes Using Domain Adaptation [link]Paper   bibtex
Improving Concept Identification for linking EHR notes to education materials. Zheng, J; and Yu, H. In Empirical Methods in Natural Language Processing, Lisboa, Portugal, 2015.
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Towards Mining Electronic Health Records for Opioid ADE Surveillance. Yu, H; Brandt, C; Becker, W; and Kem, R In The 2015 HSR&D/QUERI National Conference, 2015.
Towards Mining Electronic Health Records for Opioid ADE Surveillance [link]Paper   bibtex   abstract
Learning to rank scientific articles. Lingerman, J; and Hong, Y. In AMIA Fall Symposium, 2015.
Learning to rank scientific articles. [pdf]Paper   bibtex
Systems for helping Veterans Comprehend their own EHR notes. Yu, H; Brandt, C; and Houston, T In 2015 HSR&D/QUERI National Conference, 2015.
Systems for helping Veterans Comprehend their own EHR notes. [link]Paper   bibtex   abstract
  2014 (5)
Learning to Rank Figures within a Biomedical Article. Liu, F.; and Yu, H. PLoS ONE, 9(3): e61567. March 2014.
Learning to Rank Figures within a Biomedical Article [link]Paper   doi   bibtex   abstract
Computational Approaches for Predicting Biomedical Research Collaborations. Zhang, Q.; and Yu, H. PLoS ONE, 9(11): e111795. November 2014.
Computational Approaches for Predicting Biomedical Research Collaborations [link]Paper   doi   bibtex   abstract
Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives. Polepalli Ramesh, B.; Belknap, S. M; Li, Z.; Frid, N.; West, D. P; and Yu, H. JMIR Medical Informatics, 2(1): e10. June 2014.
Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives [link]Paper   doi   bibtex
A robust data-driven approach for gene ontology annotation. Li, Y.; and Yu, H. Database: The Journal of Biological Databases and Curation, 2014: bau113. 2014. 00000
A robust data-driven approach for gene ontology annotation [link]Paper   doi   bibtex   abstract
Treatment of acute venous thromboembolism with dabigatran or warfarin and pooled analysis. Schulman, S.; Kakkar, A. K.; Goldhaber, S. Z.; Schellong, S.; Eriksson, H.; Mismetti, P.; Christiansen, A. V.; Friedman, J.; Le Maulf, F.; Peter, N.; Kearon, C.; and RE-COVER II Trial Investigators Circulation, 129(7): 764–772. February 2014.
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  2013 (2)
Systems for Improving Electronic Health Record Note Comprehension. Polepalli Ramesh, B.; and Yu, H. In ACM SIGIR Workshop on Health Search & Discovery, 2013.
Systems for Improving Electronic Health Record Note Comprehension [pdf]Paper   bibtex   abstract
CiteGraph: A Citation Network System for MEDLINE Articles and Analysis. Qing, Z.; and Hong, Y. Studies in Health Technology and Informatics,832–836. 2013.
CiteGraph: A Citation Network System for MEDLINE Articles and Analysis [link]Paper   doi   bibtex   abstract
  2012 (4)
Beyond Captions: Linking Figures with Abstract Sentences in Biomedical Articles. Bockhorst, J. P.; Conroy, J. M.; Agarwal, S.; O’Leary, D. P.; and Yu, H. PLoS ONE, 7(7): e39618. July 2012.
Beyond Captions: Linking Figures with Abstract Sentences in Biomedical Articles [link]Paper   doi   bibtex
Automatic discourse connective detection in biomedical text. Ramesh, B. P.; Prasad, R.; Miller, T.; Harrington, B.; and Yu, H. Journal of the American Medical Informatics Association: JAMIA, 19(5): 800–808. October 2012.
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Natural Language Processing, Electronic Health Records, and Clinical Research. Liu, F.; Weng, C.; and Yu, H. In Clinical Research Informatics, pages 293–310. Springer London, 2012.
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MedTxting: learning based and knowledge rich SMS-style medical text contraction. Liu, F.; Moosavinasab, S.; Houston, T. K.; and Yu, H. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012: 558–567. 2012.
MedTxting: learning based and knowledge rich SMS-style medical text contraction [link]Paper   bibtex   abstract
  2011 (12)
BioN∅T: A searchable database of biomedical negated sentences. Agarwal, S.; Yu, H.; and Kohane, I. BMC Bioinformatics, 12(1): 420. 2011.
BioN∅T: A searchable database of biomedical negated sentences [link]Paper   doi   bibtex
AskHERMES: An online question answering system for complex clinical questions. Cao, Y.; Liu, F.; Simpson, P.; Antieau, L.; Bennett, A.; Cimino, J. J; Ely, J.; and Yu, H. Journal of Biomedical Informatics, 44(2): 277–288. April 2011.
AskHERMES: An online question answering system for complex clinical questions [link]Paper   doi   bibtex   abstract
Toward automated consumer question answering: Automatically separating consumer questions from professional questions in the healthcare domain. Liu, F.; Antieau, L. D.; and Yu, H. Journal of Biomedical Informatics, 44(6): 1032–1038. December 2011.
Toward automated consumer question answering: Automatically separating consumer questions from professional questions in the healthcare domain [link]Paper   doi   bibtex   abstract
Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions. Agarwal, S.; Liu, F.; and Yu, H. BMC Bioinformatics, 12(Suppl 8): S10. 2011.
Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions [link]Paper   doi   bibtex   abstract
Parsing citations in biomedical articles using conditional random fields. Zhang, Q.; Cao, Y.; and Yu, H. Computers in Biology and Medicine, 41(4): 190–194. April 2011.
Parsing citations in biomedical articles using conditional random fields [link]Paper   doi   bibtex   abstract
Figure Text Extraction in Biomedical Literature. Kim, D.; and Yu, H. PLoS ONE, 6(1): e15338. January 2011.
Figure Text Extraction in Biomedical Literature [link]Paper   doi   bibtex
Automatic figure classification in bioscience literature. Kim, D.; Ramesh, B. P.; and Yu, H. Journal of Biomedical Informatics, 44(5): 848–858. October 2011.
Automatic figure classification in bioscience literature [link]Paper   doi   bibtex
An investigation into the feasibility of spoken clinical question answering. Miller, T.; Ravvaz, K.; Cimino, J. J.; and Yu, H. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011: 954–959. 2011.
An investigation into the feasibility of spoken clinical question answering [link]Paper   bibtex   abstract
Apixaban versus warfarin in patients with atrial fibrillation. Granger, C. B.; Alexander, J. H.; McMurray, J. J. V.; Lopes, R. D.; Hylek, E. M.; Hanna, M.; Al-Khalidi, H. R.; Ansell, J.; Atar, D.; Avezum, A.; Bahit, M. C.; Diaz, R.; Easton, J. D.; Ezekowitz, J. A.; Flaker, G.; Garcia, D.; Geraldes, M.; Gersh, B. J.; Golitsyn, S.; Goto, S.; Hermosillo, A. G.; Hohnloser, S. H.; Horowitz, J.; Mohan, P.; Jansky, P.; Lewis, B. S.; Lopez-Sendon, J. L.; Pais, P.; Parkhomenko, A.; Verheugt, F. W. A.; Zhu, J.; Wallentin, L.; ARISTOTLE Committees; and Investigators The New England Journal of Medicine, 365(11): 981–992. September 2011.
Apixaban versus warfarin in patients with atrial fibrillation [link]Paper   doi   bibtex   abstract
Figure summarizer browser extensions for PubMed Central. Agarwal, S.; and Yu, H. Bioinformatics, 27(12): 1723–1724. June 2011.
Figure summarizer browser extensions for PubMed Central [link]Paper   doi   bibtex
The biomedical discourse relation bank. Prasad, R.; McRoy, S.; Frid, N.; Joshi, A.; and Yu, H. BMC Bioinformatics, 12(1): 188. May 2011.
The biomedical discourse relation bank [link]Paper   doi   bibtex   abstract
Towards spoken clinical-question answering: evaluating and adapting automatic speech-recognition systems for spoken clinical questions. Liu, F.; Tur, G.; Hakkani-Tür, D.; and Yu, H. Journal of the American Medical Informatics Association: JAMIA, 18(5): 625–630. October 2011.
Towards spoken clinical-question answering: evaluating and adapting automatic speech-recognition systems for spoken clinical questions [link]Paper   doi   bibtex   abstract
  2010 (8)
Lancet: a high precision medication event extraction system for clinical text. Li, Z.; Liu, F.; Antieau, L.; Cao, Y.; and Yu, H. Journal of the American Medical Informatics Association: JAMIA, 17(5): 563–567. October 2010.
Lancet: a high precision medication event extraction system for clinical text [link]Paper   doi   bibtex   abstract
Identifying discourse connectives in biomedical text. Ramesh, B. P.; and Yu, H. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010: 657–661. November 2010.
Identifying discourse connectives in biomedical text [link]Paper   bibtex   abstract