Risk Stratification of ICU Patients Using Topic Models Inferred from Unstructured Progress Notes

By Li-wei H. Lehman, Mohammed Saeed, William J. Long, Joon Lee, R. Mark. 2012

102 citations recorded by the source corpus.

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[106], [142], [178], [179], applied topic models and extended tensor-based topic models to learn better coherent features.”
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data (2015, background): “This is usually accomplished by windowing or aggregating the structured numerical data so that a single feature matrix can be fed into a structured deterministic classifier (Hug and Szolovits 2009; Lehman et al. 2012; Joshi and Szolovits 2012; Ghassemi et al. 2014).”
Unfolding Physiological State: Mortality Modelling in Intensive Care Units (2014, methodology): “[11] applied Hierarchical Dirichlet Processes to nursing notes from the first 24 hours for ICU patient risk stratification.”
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets (2017, background): “The availability of large healthcare databases such as Medical Information Mart for Intensive Care (MIMIC-II and III) databases [4, 5] has accelerated the research in this important area as evidenced by a lot of recent publications [6, 7, 8, 2, 9, 10, 11, 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 2”
Machine Learning and Decision Support in Critical Care (2016, methodology): “[199] used a hierarchical Dirichlet process (HDP) to perform patient risk stratification by combining physiologic data and topics learned from unstructured clinical notes.”