Deep Computational Phenotyping

By Zhengping Che, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, Yan Liu. 2015

267 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Signal Processing

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Citations in the corpus

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017, background): “(Miotto et al. 2016; Choi et al. 2016; Che et al. 2015)), the clinical community remains skeptical of machine learning systems”
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain (2015, methodology): “With the flexibility and power of all kinds of neural networks, some deep network variants are also potentially suitable for healthcare tasks [9, 30].”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[164] also applied deep learningmethods to study time series in ICU data.”
Learning to Diagnose with LSTM Recurrent Neural Networks (2015, methodology): “They previously have been shown to be effective for these data (Marlin et al., 2012; Che et al., 2015).”
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (2015, neutral): “We make the source code of Doctor AI publicly available at https://github.com/mp2893/doctorai .”
GRAM: Graph-based Attention Model for Healthcare Representation Learning (2016, methodology): “3098126 networks (RNN) [7, 9, 10, 25], convolutional neural networks (CNN) [30] or stacked denoising autoencoders (SDA) [6, 29], demonstrating signi cant performance enhancement for diverse prediction tasks.”
Text Graph Transformer for Document Classification (2020, background): “Text classiﬁcation is a widely studied problem in natural language processing and has been addressed in many real-world applications such as news ﬁltering, spam detection, and health record systems (Kowsari et al., 2019; Che et al., 2015; Zhang et al., 2018).”
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks (2015, methodology): “nomics [8, 10, 9]. A variety of works have applied feed-forward neural networks to health data for prediction and pattern mining, but none have used RNNs or directly handled variable length sequences [17, 18, 19, 20]. To our knowledge, this work is the ﬁrst to apply modern LSTMs to a large data set ”
Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting (2017, background): “We note interesting observations from the trained neural network.”
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”
Large-Scale Discovery of Disease-Disease and Disease-Gene Associations (2016, methodology): “In addition, to create more scalable, effective tools, recent approaches distinct from networks have focused on the development of data-driven phenotyping with minimal manual input and rigorous evaluation procedures 3,30,31 .”
Modeling Missing Data in Clinical Time Series with RNNs (2016, methodology): “(2016), addressing a dataset and problem described by Che et al. (2015). The methods rely upon LSTM RNNs (Hochreiter and Schmidhuber, 1997; Gers et al., 2000) trained by backpropagation through time (Hinton et al., 2006; Werbos, 1988). A comprehensive perspective on the history and modern applicatio”
Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding (2017, methodology): “[17] used stacked denoising autoencoders (SDAs) regularized with a prior knowledge based on ICD-9 to detect characteristic patterns of physiology.”
Building a knowledge graph to enable precision medicine (2022, background): “…open the doors for many types of research to leverage recent computational advances, including but not limited to the study of disease phenotyping 5–7 , disease etiology 8 , disease similarity 9 , disease diagnosis 10–12 , disease treatments 13 , drug-disease relationships 14–16 , mechanisms of…”
Mining Electronic Health Records: A Survey (2017, background): “; Che et al. 2015; Albers et al. 2014] Risk Prediction/Biomarker Discovery Descritive Atemporal [Gotz et al. 2011a; Vellanki et al. 2014; Miotto et al. 2016] Descritive Temporal Cross-Sectional [Wilcox and Hripcsak 2003; Sarkar et al. 2012; Letham et al. 2013; Ebadol-lahi et al. 2010; Feldman and…”
Deep learning for healthcare: review, opportunities and challenges (2018, unknown): “[63] used SDAs regularized with a prior knowledge based on ICD-9s for detecting characteristic patterns of physiology in clinical time series.”