$\mathtt {Deepr}$: A Convolutional Net for Medical Records

By Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh. 2016

400 citations recorded by the source corpus.

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive <italic>regular clinical motifs</italic> from <italic> irregular episodic records</italic>. We present <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math> </inline-formula> (short for <italic>Deep</italic> <italic>r</italic>ecord), a new <italic>end-to-end</italic> deep learning system that learns to extract features from medical records and predicts future risk automatically. <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math></inline-formula> transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math></inline-formula> permits transparent inspection and visualization of its inner working. We validate <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$ </tex-math></inline-formula> on hospital data to predict unplanned readmission after discharge. <inline-formula> <tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math></inline-formula> achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.

Subfields: Artificial Intelligence, Health Information Management

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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.”
GRAM: Graph-based Attention Model for Healthcare Representation Learning (2016, background): “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.”
BEHRT: Transformer for Electronic Health Records (2019, methodology): “In attempt to address this, Nguyen et al [13] introduced a convolutional neural network (CNN) model called Deepr (Deep record) for predicting the probability of readmission; they treated one’s medical history as a sequence of concepts (e.”
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis (2017, methodology): “DeepPatient Multi-outcome Prediction Miotto [15] Deepr Hospital Re-admission Prediction Nguyen [20] DeepCare EHR Concept Representation Pham [21] Doctor AI Heart Failure Prediction Choi [22] Med2Vec EHR Concept Representation Choi [23] eNRBM Suicide risk stratification Tran [24]”
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models (2018, methodology): “ clinical informatics [36]. For example, deep learning has been used to learn effective representations for patient records [14, 26] to support disease phenotyping [8] and conduct predictive modeling [9, 30, 33]. A recent study from Google demonstrated the capability of deep learning methods on pred”
Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (2017, background): “Convolutional neural networks (CNNs) are used to predict unplanned readmission [23] and risk [7] with EHR.”
Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network (2017, methodology): “employed convolutional neural networks to extract “clinical motifs” from medical records and predict clinical outcomes [27].”
Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval (2024, unknown): “We compare to three categories of baselines: (1) ML-based methods: GRU (Chung et al., 2014), Transformer (Vaswani et al., 2017), RETAIN (Choi et al., 2016), GRAM (Choi et al., 2017), Deepr (Nguyen et al., 2016), TCN (Bai et al., 2018), StageNet (Gao et al., 2020), ConCare (Ma et al., 2020b), AdaCare”