Tristan Naumann
Tristan Naumann
Principal Researcher, Microsoft Research Healthcare NExT
Verified email at - Homepage
Cited by
Cited by
Publicly available clinical BERT embeddings
E Alsentzer, JR Murphy, W Boag, WH Weng, D Jin, T Naumann, ...
arXiv preprint arXiv:1904.03323, 2019
Unfolding physiological state: Mortality modelling in intensive care units
M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, ...
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data
M Ghassemi, M Pimentel, T Naumann, T Brennan, D Clifton, P Szolovits, ...
Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015
Secondary analysis of electronic health records
MIT Critical Data
Springer Nature, 2016
Predicting early psychiatric readmission with natural language processing of narrative discharge summaries
A Rumshisky, M Ghassemi, T Naumann, P Szolovits, VM Castro, ...
Translational psychiatry 6 (10), e921-e921, 2016
Domain-specific language model pretraining for biomedical natural language processing
Y Gu, R Tinn, H Cheng, M Lucas, N Usuyama, X Liu, T Naumann, J Gao, ...
arXiv preprint arXiv:2007.15779, 2020
Making big data useful for health care: a summary of the inaugural mit critical data conference
O Badawi, T Brennan, LA Celi, M Feng, M Ghassemi, A Ippolito, ...
JMIR medical informatics 2 (2), e22, 2014
Natural language processing for EHR-based computational phenotyping
Z Zeng, Y Deng, X Li, T Naumann, Y Luo
IEEE/ACM transactions on computational biology and bioinformatics 16 (1 …, 2018
Opportunities in machine learning for healthcare
M Ghassemi, T Naumann, P Schulam, AL Beam, R Ranganath
arXiv preprint arXiv:1806.00388, 2018
Practical guidance on artificial intelligence for health-care data
M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath
The Lancet Digital Health 1 (4), e157-e159, 2019
A “datathon” model to support cross-disciplinary collaboration
J Aboab, LA Celi, P Charlton, M Feng, M Ghassemi, DC Marshall, ...
Science Translational Medicine 8 (333), 333ps8-333ps8, 2016
CliNER: A lightweight tool for clinical named entity recognition
W Boag, K Wacome, T Naumann, A Rumshisky
AMIA joint summits on clinical research informatics (poster), 2015
A review of challenges and opportunities in machine learning for health
M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath
AMIA Summits on Translational Science Proceedings 2020, 191, 2020
Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks
B Nestor, MBA McDermott, W Boag, G Berner, T Naumann, MC Hughes, ...
Machine Learning for Healthcare Conference, 381-405, 2019
What’s in a note? unpacking predictive value in clinical note representations
W Boag, D Doss, T Naumann, P Szolovits
AMIA Summits on Translational Science Proceedings 2018, 26, 2018
Predicting clinical outcomes across changing electronic health record systems
JJ Gong, T Naumann, P Szolovits, JV Guttag
proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017
Cliner 2.0: Accessible and accurate clinical concept extraction
W Boag, E Sergeeva, S Kulshreshtha, P Szolovits, A Rumshisky, ...
arXiv preprint arXiv:1803.02245, 2018
Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii
S Wang, MBA McDermott, G Chauhan, M Ghassemi, MC Hughes, ...
Proceedings of the ACM Conference on Health, Inference, and Learning, 222-235, 2020
Trends and focus of machine learning applications for health research
B Beaulieu-Jones, SG Finlayson, C Chivers, I Chen, M McDermott, ...
JAMA network open 2 (10), e1914051-e1914051, 2019
Topic models for mortality modeling in intensive care units
M Ghassemi, T Naumann, R Joshi, A Rumshisky
ICML 2012 Machine Learning for Clinical Data Analysis Workshop, 2012
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