Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Altosaar, Jaan"'
Autor:
Zelko, Jacob S., Gasman, Sarah, Freeman, Shenita R., Lee, Dong Yun, Altosaar, Jaan, Shoaibi, Azza, Rao, Gowtham
Health informatics can inform decisions that practitioners, patients, policymakers, and researchers need to make about health and disease. Health informatics is built upon patient health data leading to the need to codify patient health information.
Externí odkaz:
http://arxiv.org/abs/2304.06504
An assisted living facility (ALF) is a place where someone can live, have access to social supports such as transportation, and receive assistance with the activities of daily living such as toileting and dressing. Despite the important role of ALFs,
Externí odkaz:
http://arxiv.org/abs/2212.14092
Disease identification is a core, routine activity in observational health research. Cohorts impact downstream analyses, such as how a condition is characterized, how patient risk is defined, and what treatments are studied. It is thus critical to en
Externí odkaz:
http://arxiv.org/abs/2203.05174
We consider the problem of evaluating representations of data for use in solving a downstream task. We propose to measure the quality of a representation by the complexity of learning a predictor on top of the representation that achieves low loss on
Externí odkaz:
http://arxiv.org/abs/2009.07368
Clinical notes contain information about patients that goes beyond structured data like lab values and medications. However, clinical notes have been underused relative to structured data, because notes are high-dimensional and sparse. This work deve
Externí odkaz:
http://arxiv.org/abs/1904.05342
Recurrent neural networks (RNNs) are powerful models of sequential data. They have been successfully used in domains such as text and speech. However, RNNs are susceptible to overfitting; regularization is important. In this paper we develop Noisin,
Externí odkaz:
http://arxiv.org/abs/1805.01500
Variational inference is a powerful approach for approximate posterior inference. However, it is sensitive to initialization and can be subject to poor local optima. In this paper, we develop proximity variational inference (PVI). PVI is a new method
Externí odkaz:
http://arxiv.org/abs/1705.08931
Variational inference is an umbrella term for algorithms which cast Bayesian inference as optimization. Classically, variational inference uses the Kullback-Leibler divergence to define the optimization. Though this divergence has been widely used, t
Externí odkaz:
http://arxiv.org/abs/1610.09033
Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models. This paper introduces two probabilistic topic models, Correlated LDA (C-LDA)
Externí odkaz:
http://arxiv.org/abs/1508.04562
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