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ORCIDTristan Naumann
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- affiliation: Microsoft Research, Redmond, WA, USA
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2020 – today
- 2024
[j6]Steven E. Labkoff
, Bilikis Oladimeji, Joseph L. Kannry, Anthony Solomonides, Russell Leftwich, Eileen Koski, Amanda L. Joseph, Monica Lopez-Gonzalez, Lee A Fleisher, Kimberly Nolen, Sayon Dutta, Deborah R. Levy, Amy Price, Paul J. Barr, Jonathan D. Hron, Baihan Lin, Gyana Srivastava, Nuria Pastor, Unai Sanchez Luque, Tien Thi Thuy Bui, Reva Singh, Tayler Williams, Mark G. Weiner, Tristan Naumann, Dean F. Sittig, Gretchen Purcell Jackson, Yuri Quintana:
Toward a responsible future: recommendations for AI-enabled clinical decision support. J. Am. Medical Informatics Assoc. 31(11): 2730-2739 (2024)- [c24]Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn P. Rosé:
DocLens: Multi-aspect Fine-grained Medical Text Evaluation. ACL (1) 2024: 649-679 - [c23]Aliyah R. Hsu, Yeshwanth Cherapanamjeri, Briton Park, Tristan Naumann, Anobel Y. Odisho, Bin Yu:
Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making. ICLR 2024 - [e7]Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle S. Bitterman:
Proceedings of the 6th Clinical Natural Language Processing Workshop, ClinicalNLP@NAACL 2024, Mexico City, Mexico, June 21, 2024. Association for Computational Linguistics 2024, ISBN 979-8-89176-109-4 [contents] - [i36]Zelalem Gero, Chandan Singh, Yiqing Xie, Sheng Zhang, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries. CoRR abs/2403.01002 (2024) - [i35]Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai Orson Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo:
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. CoRR abs/2403.01628 (2024) - [i34]Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu, Hanwen Xu, Naoto Usuyama, Sheng Zhang, Fei Wang, Yujia Xie, Mahmoud Khademi, Ziyi Yang, Hany Hassan Awadalla, Julia Gong, Houdong Hu, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Yu Gu, Cliff Wong, Mu Wei, Tristan Naumann, Muhao Chen, Matthew P. Lungren, Serena Yeung-Levy, Curtis P. Langlotz, Sheng Wang, Hoifung Poon:
Training Small Multimodal Models to Bridge Biomedical Competency Gap: A Case Study in Radiology Imaging. CoRR abs/2403.08002 (2024) - [i33]Theodore Zhao, Yu Gu, Jianwei Yang, Naoto Usuyama, Ho Hin Lee, Tristan Naumann, Jianfeng Gao, Angela Crabtree, Jacob Abel, Christine Moung, Brian Piening, Carlo Bifulco, Mu Wei, Hoifung Poon, Sheng Wang:
BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once. CoRR abs/2405.12971 (2024) - 2023
- [j5]Sam Preston, Mu Wei, Rajesh Rao, Robert Tinn, Naoto Usuyama, Michael Lucas, Yu Gu, Roshanthi Weerasinghe, Soohee Lee, Brian Piening, Paul Tittel, Naveen Valluri, Tristan Naumann, Carlo Bifulco, Hoifung Poon:
Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision. Patterns 4(4): 100726 (2023) - [j4]Robert Tinn, Hao Cheng, Yu Gu, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Fine-tuning large neural language models for biomedical natural language processing. Patterns 4(4): 100729 (2023) - [j3]Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya V. Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland:
Compositional Zero-Shot Domain Transfer with Text-to-Text Models. Trans. Assoc. Comput. Linguistics 11: 1097-1113 (2023) - [c22]Griffin Adams, Bichlien Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad:
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization. ACL (1) 2023: 10520-10542 - [c21]Wenxuan Zhou, Sheng Zhang, Tristan Naumann, Muhao Chen, Hoifung Poon:
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction. ACL (1) 2023: 13249-13263 - [c20]Keming Lu, Peter Potash, Xihui Lin, Yuwen Sun, Zihan Qian, Zheng Yuan, Tristan Naumann, Tianxi Cai, Junwei Lu:
Prompt Discriminative Language Models for Domain Adaptation. ClinicalNLP@ACL 2023: 247-258 - [c19]Hoifung Poon, Tristan Naumann, Sheng Zhang, Javier González Hernández:
Precision Health in the Age of Large Language Models. KDD 2023: 5825-5826 - [c18]Cliff Wong, Sheng Zhang, Yu Gu, Christine Moung, Jacob Abel, Naoto Usuyama, Roshanthi Weerasinghe, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon:
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology. MLHC 2023: 846-862 - [c17]Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao:
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day. NeurIPS 2023 - [e6]Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Anna Rumshisky:
Proceedings of the 5th Clinical Natural Language Processing Workshop, ClinicalNLP@ACL 2023, Toronto, Canada, July 14, 2023. Association for Computational Linguistics 2023, ISBN 978-1-959429-88-3 [contents] - [e5]Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine:
Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. 2023 [contents] - [i32]Sheng Zhang, Yanbo Xu, Naoto Usuyama, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Matthew P. Lungren, Tristan Naumann, Hoifung Poon:
Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing. CoRR abs/2303.00915 (2023) - [i31]Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya V. Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland:
Compositional Zero-Shot Domain Transfer with Text-to-Text Models. CoRR abs/2303.13386 (2023) - [i30]Griffin Adams, Bichlien H. Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad:
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization. CoRR abs/2305.07615 (2023) - [i29]Aliyah R. Hsu, Yeshwanth Cherapanamjeri, Briton Park, Tristan Naumann, Anobel Y. Odisho, Bin Yu:
An Investigation into the Effects of Pre-training Data Distributions for Pathology Report Classification. CoRR abs/2305.17588 (2023) - [i28]Zelalem Gero, Chandan Singh, Hao Cheng, Tristan Naumann, Michel Galley, Jianfeng Gao, Hoifung Poon:
Self-Verification Improves Few-Shot Clinical Information Extraction. CoRR abs/2306.00024 (2023) - [i27]Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao:
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day. CoRR abs/2306.00890 (2023) - [i26]Yu Gu, Sheng Zhang, Naoto Usuyama, Yonas Woldesenbet, Cliff Wong, Praneeth Sanapathi, Mu Wei, Naveen Valluri, Erika Strandberg, Tristan Naumann, Hoifung Poon:
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events. CoRR abs/2307.06439 (2023) - [i25]Cliff Wong, Sheng Zhang, Yu Gu, Christine Moung, Jacob Abel, Naoto Usuyama, Roshanthi Weerasinghe, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon:
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology. CoRR abs/2308.02180 (2023) - [i24]Javier González Hernández, Cliff Wong, Zelalem Gero, Jass Bagga, Risa Ueno, Isabel Chien, Eduard Oravkin, Emre Kiciman, Aditya V. Nori, Roshanthi Weerasinghe, Rom S. Leidner, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon:
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models. CoRR abs/2311.01301 (2023) - [i23]Yiqing Xie, Sheng Zhang, Hao Cheng, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon:
Enhancing Medical Text Evaluation with GPT-4. CoRR abs/2311.09581 (2023) - 2022
- [j2]Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. ACM Trans. Comput. Heal. 3(1): 2:1-2:23 (2022) - [c16]Gerardo Flores, George H. Chen, Tom J. Pollard, Ayah Zirikly, Michael C. Hughes, Tasmie Sarker, Joyce C. Ho, Tristan Naumann:
Conference on Health, Inference, and Learning (CHIL) 2022. CHIL 2022: 1-4 - [c15]Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie L. Hyland, Maria Wetscherek, Tristan Naumann, Aditya V. Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay:
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing. ECCV (36) 2022: 1-21 - [c14]Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Knowledge-Rich Self-Supervision for Biomedical Entity Linking. EMNLP (Findings) 2022: 868-880 - [e4]Gerardo Flores, George H. Chen, Tom J. Pollard, Joyce C. Ho, Tristan Naumann:
Conference on Health, Inference, and Learning, CHIL 2022, 7-8 April 2022, Virtual Event. Proceedings of Machine Learning Research 174, PMLR 2022 [contents] - [i22]Sam Preston, Mu Wei, Rajesh Rao, Robert Tinn, Naoto Usuyama, Michael Lucas, Roshanthi Weerasinghe, Soohee Lee, Brian Piening, Paul Tittel, Naveen Valluri, Tristan Naumann, Carlo Bifulco, Hoifung Poon:
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision. CoRR abs/2203.10442 (2022) - [i21]Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie L. Hyland, Maria Wetscherek, Tristan Naumann, Aditya V. Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay:
Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing. CoRR abs/2204.09817 (2022) - [i20]Gerardo Flores, George H. Chen, Tom J. Pollard, Joyce C. Ho, Tristan Naumann:
A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022. CoRR abs/2205.02752 (2022) - [i19]Wenxuan Zhou, Sheng Zhang, Tristan Naumann, Muhao Chen, Hoifung Poon:
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction. CoRR abs/2212.10823 (2022) - 2021
- [c13]Sheng Zhang, Cliff Wong, Naoto Usuyama, Sarthak Jain, Tristan Naumann, Hoifung Poon:
Modular Self-Supervision for Document-Level Relation Extraction. EMNLP (1) 2021: 5291-5302 - [c12]Yu Wang, Jinchao Li, Tristan Naumann, Chenyan Xiong, Hao Cheng, Robert Tinn, Cliff Wong, Naoto Usuyama, Richard Rogahn, Zhihong Shen, Yang Qin, Eric Horvitz, Paul N. Bennett, Jianfeng Gao, Hoifung Poon:
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature. KDD 2021: 3717-3725 - [e3]Marzyeh Ghassemi, Tristan Naumann, Emma Pierson:
ACM CHIL '21: ACM Conference on Health, Inference, and Learning, Virtual Event, USA, April 8-9, 2021. ACM 2021, ISBN 978-1-4503-8359-2 [contents] - [i18]Yu Wang, Jinchao Li, Tristan Naumann, Chenyan Xiong, Hao Cheng, Robert Tinn, Cliff Wong, Naoto Usuyama, Richard Rogahn, Zhihong Shen, Yang Qin, Eric Horvitz, Paul N. Bennett, Jianfeng Gao, Hoifung Poon:
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature. CoRR abs/2106.13375 (2021) - [i17]Sheng Zhang, Cliff Wong, Naoto Usuyama, Sarthak Jain, Tristan Naumann, Hoifung Poon:
Modular Self-Supervision for Document-Level Relation Extraction. CoRR abs/2109.05362 (2021) - [i16]Robert Tinn, Hao Cheng, Yu Gu, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing. CoRR abs/2112.07869 (2021) - [i15]Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Knowledge-Rich Self-Supervised Entity Linking. CoRR abs/2112.07887 (2021) - 2020
- [c11]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, Tristan Naumann:
MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. CHIL 2020: 222-235 - [c10]Shems Saleh, William Boag, Lauren Erdman, Tristan Naumann:
Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration. MLHC 2020: 783-812 - [e2]Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann:
Proceedings of the 3rd Clinical Natural Language Processing Workshop, ClinicalNLP@EMNLP 2020, Online, November 19, 2020. Association for Computational Linguistics 2020, ISBN 978-1-952148-74-3 [contents] - [i14]Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca:
ML4H Abstract Track 2019. CoRR abs/2002.01584 (2020) - [i13]Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. CoRR abs/2007.15779 (2020)
2010 – 2019
- 2019
- [j1]Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo:
Natural Language Processing for EHR-Based Computational Phenotyping. IEEE ACM Trans. Comput. Biol. Bioinform. 16(1): 139-153 (2019) - [c9]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. MLHC 2019: 381-405 - [c8]Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Corey Chivers, Andrew Beam, Tristan Naumann, Brett K. Beaulieu-Jones:
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? ML4H@NeurIPS 2019: 1-9 - [c7]Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi:
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. ML4H@NeurIPS 2019: 202-219 - [i12]Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, Matthew B. A. McDermott:
Publicly Available Clinical BERT Embeddings. CoRR abs/1904.03323 (2019) - [i11]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi:
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III. CoRR abs/1907.08322 (2019) - [i10]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. CoRR abs/1908.00690 (2019) - [i9]Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi:
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. CoRR abs/1912.04370 (2019) - 2018
- [b1]Tristan Naumann:
Leveraging text representations for clinical predictive tasks. Massachusetts Institute of Technology, Cambridge, USA, 2018 - [c6]Matthew B. A. McDermott, Tom Yan, Tristan Naumann, Nathan Hunt, Harini Suresh, Peter Szolovits, Marzyeh Ghassemi:
Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs. AAAI 2018: 2363-2370 - [i8]Willie Boag, Elena Sergeeva, Saurabh Kulshreshtha, Peter Szolovits, Anna Rumshisky, Tristan Naumann:
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction. CoRR abs/1803.02245 (2018) - [i7]Willie Boag, Tristan Naumann, Peter Szolovits:
Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes. CoRR abs/1803.02728 (2018) - [i6]Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath:
Opportunities in Machine Learning for Healthcare. CoRR abs/1806.00388 (2018) - [i5]Dina Levy-Lambert, Jen J. Gong, Tristan Naumann, Tom J. Pollard, John V. Guttag:
Visualizing Patient Timelines in the Intensive Care Unit. CoRR abs/1806.00397 (2018) - [i4]Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo:
Natural Language Processing for EHR-Based Computational Phenotyping. CoRR abs/1806.04820 (2018) - [i3]Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian V. Dalca, Samuel G. Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew B. A. McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi:
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. CoRR abs/1811.07216 (2018) - [i2]Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation. CoRR abs/1811.12583 (2018) - [i1]Alistair E. W. Johnson, Tom J. Pollard, Tristan Naumann:
Generalizability of predictive models for intensive care unit patients. CoRR abs/1812.02275 (2018) - 2017
- [c5]Jen J. Gong, Tristan Naumann, Peter Szolovits, John V. Guttag:
Predicting Clinical Outcomes Across Changing Electronic Health Record Systems. KDD 2017: 1497-1505 - 2016
- [e1]Anna Rumshisky, Kirk Roberts, Steven Bethard, Tristan Naumann:
Proceedings of the Clinical Natural Language Processing Workshop, ClinicalNLP@COLING 2016, Osaka, Japan, December 11, 2016. The COLING 2016 Organizing Committee 2016, ISBN 978-4-87974-710-5 [contents] - 2015
- [c4]Marzyeh Ghassemi, Marco A. F. Pimentel, Tristan Naumann, Thomas Brennan, David A. Clifton, Peter Szolovits, Mengling Feng:
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data. AAAI 2015: 446-453 - 2014
- [c3]Tristan Naumann, Ikaro Silva:
Scaling the PhysioNet WFDB Toolbox for MATLAB and Octave. CinC 2014: 161-164 - [c2]Marzyeh Ghassemi, Tristan Naumann, Finale Doshi-Velez, Nicole Brimmer, Rohit Joshi, Anna Rumshisky, Peter Szolovits:
Unfolding physiological state: mortality modelling in intensive care units. KDD 2014: 75-84 - 2013
- [c1]Tristan Naumann, Marzyeh Ghassemi, Andreea Bodnari, Rohit Joshi:
Probabilistically Populated Medical Record Templates: Reducing Clinical Documentation Time Using Patient Cooperation. AMIA 2013
Coauthor Index
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