title: $\mathtt {Deepr}$: A Convolutional Net for Medical Records.
abstract: Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr 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. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.
authors.forename: Phuoc
authors.initials: P
authors.lastname: Nguyen
authors.forename: Truyen
authors.initials: T
authors.lastname: Tran
authors.forename: Nilmini
authors.initials: N
authors.lastname: Wickramasinghe
authors.forename: Svetha
authors.initials: S
authors.lastname: Venkatesh
mesh_terms: D001185:Artificial Intelligence; D016208:Databases, Factual; D057286:Electronic Health Records; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012984:Software