Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests

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

110 citations recorded by the source corpus.

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Citations in the corpus

Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018, methodology): “Many other convolutional neural networks [4, 21] and recurrent neural networks [9, 25] have been applied to modeling EHR data for different prediction tasks, but their focus is slightly outside of the scope of this paper.”
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU (2018, methodology): “n to the outcome of interest so that separate task models are beneficial. Task formulations for multi-task learning with clinical data fall into two categories: 1) distinct outcomes are used as tasks [4, 18, 34, 42] and 2) distinct patient populations are used as tasks [30, 46]. Our formulation fall”
Deep learning for healthcare: review, opportunities and challenges (2018, unknown): “[67] evaluated CNNs and RNNs with LSTM units to predict disease onset from laboratory test measures alone, showing better performances than logistic regression with hand-engineered, clinically relevant features.”
Multi-task Self-Supervised Learning for Human Activity Detection (2019, methodology): “ehavioral context in a real-life se−ing. Moreover, Oh et al. [48] focused on learning invariances directly from clinical time-series data with specialized neural network architecture. Razavian et al. [59] used convolution layers of varying size •lters to capture di‡erent resolutions of temporal pa−e”
Disease-Atlas: Navigating Disease Trajectories using Deep Learning (2018, methodology): “This includes joint architectures for forecasting follow-up times and clinical diagnoses (Choi et al., 2016; Du et al., 2016) and multitask learning (Harutyunyan et al., 2017; Razavian et al., 2016; Lipton et al., 2016).”