Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests

By Narges Razavian, Jake Marcus, David A. Sontag. 2016

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “[83] used a set of 18 common lab tests to predict disease onset using both CNN and long short-term memory (LSTM) architectures and demonstrated an improvement over baseline regression models.”
Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, positive): “[42] used LSTMs to predict onset of 133 diseases and conditions simultaneously based on 18 common lab tests measured over time.”
GRAM: Graph-based Attention Model for Healthcare Representation Learning (2016, background): “Deep learning models appear to perform signiﬁcantly better than logistic regression or multilayer perceptron (MLP) models that depend, to some degree, on expert feature construction (Lipton et al., 2015; Razavian et al., 2016).”
Scalable and accurate deep learning with electronic health records (2018, background): “eling the temporal sequence of events that occurred in a patient’s record, which may enhance accuracy in scenarios that depend on the order of events, with convolutional and recurrent neural networks [61, 2, 17, 81, 16, 92]. In general, prior work has focused on a subset of features available in the”
Multitask learning and benchmarking with clinical time series data (2017, background): “ prede˙ned similarity between diseases (based on an ontology) into an explicit graph Laplacian regularizer for mortality prediction and phenotype classi˙cation. Lipton et al. [38] and Razavian et al. [54] formulate phenotyping as a multi-label classi˙cation problem and use neural networks to implici”