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Many predictive tasks, such as diagnosing a patient based on their medical chart, are ultimately defined by the decisions of human experts.
Information theoretical analysis of multivariate correlation
Watanabe, Satosi · 1960
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Constructing biological knowledge bases by extracting information from text sources
Craven, Mark and Kumlien, Johan · 1999
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The information bottleneck method
Tishby, Naftali, Pereira, Fernando C, and Bialek, William · 2000
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A simple algorithm for identifying negated findings and diseases in discharge summaries
Chapman, Wendy W., Bridewell, Will, Hanbury, Paul, Cooper, Gregory F., and Buchanan, Bruce G · 2001
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Constrained k-means clustering with background knowledge
Wagstaff, Kiri, Cardie, Claire, Rogers, Seth, Schrödl, Stefan, et al · 2001
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Multivariate information bottleneck
Slonim, Noam, Friedman, Nir, and Tishby, Naftali · 2006
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An efficient solution for mapping free text to ontology terms
Dai, Manhong, Shah, Nigam H, Xuan, Wei, Musen, Mark A, Watson, Stanley J, Athey, Brian D, Meng, Fan, et al · 2008
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Latent dirichlet allocation with topic-in-set knowledge
Andrzejewski, David and Zhu, Xiaojin · 2009
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Incorporating domain knowledge into topic modeling via dirichlet forest priors
Andrzejewski, David, Zhu, Xiaojin, and Craven, Mark · 2009
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Distant supervision for relation extraction without labeled data
Mintz, Mike, Bills, Steven, Snow, Rion, and Jurafsky, Dan · 2009
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Recognizing obesity and comorbidities in sparse data
Uzuner, Özlem · 2009
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Learning topic models – going beyond svd
Arora, Sanjeev, Ge, Rong, and Moitra, Ankur · 2012
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Incorporating lexical priors into topic models
Jagarlamudi, Jagadeesh, Daumé III, Hal, and Udupa, Raghavendra · 2012
Cited alongside, same era.
Applying active learning to high-throughput phenotyping algorithms for electronic health records data
Chen, Yukun, Carroll, Robert J, Hinz, Eugenia R McPeek, Shah, Anushi, Eyler, Anne E, Denny, Joshua C, and Xu, Hua · 2013
Cited alongside, same era.
Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the emerge network
Anchors regularized: Adding robustness and extensibility to scalable topic-modeling algorithms
Nguyen, Thang, Hu, Yuening, and Boyd-Graber, Jordan L · 2014
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Discovering structure in high-dimensional data through correlation explanation
Ver Steeg, Greg and Galstyan, Aram · 2014
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Progressive EM for latent tree models and hierarchical topic detection
Chen, Peixian, Zhang, Nevin L, Poon, Leonard KM, and Chen, Zhourong · 2015
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Anchored Discrete Factor Analysis
Halpern, Y., Horng, S., and Sontag, D · 2015
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Disentangling the lexicons of disaster response in twitter
Hodas, Nathan, Ver Steeg, Greg, Harrison, Joshua, Chikkagoudar, Satish, Bell, Eric, and Corley, Courtney · 2015
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Newton, Katherine M, Peissig, Peggy L, Kho, Abel Ngo, Bielinski, Suzette J, Berg, Richard L, Choudhary, Vidhu, Basford, Melissa, Chute, Christopher G, Kullo, Iftikhar J, Li, Rongling, Pacheco, Jennifer A, Rasmussen, Luke V, Spangler, Leslie, and Denny, Joshua C · 2013
Cited alongside, same era.
Using anchors to estimate clinical state without labeled data
Halpern, Yoni, Choi, Youngduck, Horng, Steven, and Sontag, David · 2014
Cited alongside, same era.
Low-dimensional embeddings for interpretable anchor-based topic inference
Lee, Moontae and Mimno, David · 2014
Cited alongside, same era.
Ver Steeg, Greg and Galstyan, Aram · 2015
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Learning statistical models of phenotypes using noisy labeled training data
Agarwal, V., Podchiyska, T., Banda, J. M., Goel, V., Leung, T. I., Minty, E. P., Sweeney, T. E., Gyang, E., and N.H., Shah · 2016
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Open source project implementing hierarchical topic models on sparse data, 2016
Ver Steeg, Greg · 2016
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