# Deepr: A Convolutional Net for Medical Records

arXiv: https://arxiv.org/abs/1607.07519 · HTML: https://mlsanity.com/paper/1607.07519/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2016.

## Built on by (most cited first, 30 of 60)

- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://mlsanity.com/paper/2305.12788/) (2023)
- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [CPLLM: Clinical Prediction with Large Language Models](https://mlsanity.com/paper/2309.11295/) (2023)
- [Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare](https://mlsanity.com/paper/2105.07542/) (2021)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2108.03625/) (2021)
- [Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs](https://mlsanity.com/paper/2112.05195/) (2021)
- [CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRT](https://mlsanity.com/paper/2404.15201/) (2024)
- [Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction](https://mlsanity.com/paper/2106.04751/) (2021)
- [An explainable Transformer-based deep learning model for the prediction of incident heart failure](https://mlsanity.com/paper/2101.11359/) (2021)
- [Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits](https://mlsanity.com/paper/2408.07084/) (2024)
- [Graph-Text Multi-Modal Pre-training for Medical Representation Learning](https://mlsanity.com/paper/2203.09994/) (2022)
- [Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records](https://mlsanity.com/paper/2202.03487/) (2022)
- [Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction](https://mlsanity.com/paper/2501.17326/) (2025)
- [TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data](https://mlsanity.com/paper/2406.10061/) (2024)
- [Multi-task Heterogeneous Graph Learning on Electronic Health Records](https://mlsanity.com/paper/2408.07569/) (2024)
- [Unveiling Discrete Clues: Superior Healthcare Predictions for Rare Diseases](https://mlsanity.com/paper/2501.16373/) (2025)
- [Supervised multi-specialist topic model with applications on large-scale electronic health record data](https://mlsanity.com/paper/2105.01238/) (2021)
- [MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning](https://mlsanity.com/paper/2107.09288/) (2021)
- [UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge](https://mlsanity.com/paper/2211.08082/) (2022)
- [Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning](https://mlsanity.com/paper/2104.01787/) (2021)
- [Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks](https://mlsanity.com/paper/2105.01159/) (2021)
- [sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion](https://mlsanity.com/paper/2211.17121/) (2022)
- [Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning](https://mlsanity.com/paper/2410.19955/) (2024)
- [AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment](https://mlsanity.com/paper/2203.02446/) (2022)
- [Large Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding](https://mlsanity.com/paper/2411.06823/) (2024)
- [Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph](https://mlsanity.com/paper/2411.19742/) (2024)
- [ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions](https://mlsanity.com/paper/2508.18313/) (2025)

## Abstract (arXiv)

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.