# Multi-layer Representation Learning for Medical Concepts

arXiv: https://arxiv.org/abs/1602.05568 · HTML: https://mlsanity.com/paper/1602.05568/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2016.

## Built on by (most cited first, 30 of 69)

- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://mlsanity.com/paper/2106.00750/) (2021)
- [Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion](https://mlsanity.com/paper/2202.04770/) (2022)
- [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://mlsanity.com/paper/2305.12788/) (2023)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [Benchmarking emergency department triage prediction models with machine learning and large public electronic health records](https://mlsanity.com/paper/2111.11017/) (2021)
- [Heterogeneous Similarity Graph Neural Network on Electronic Health Records](https://mlsanity.com/paper/2101.06800/) (2021)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2108.03625/) (2021)
- [High-Dimension Human Value Representation in Large Language Models](https://mlsanity.com/paper/2404.07900/) (2024)
- [Foundation Model of Electronic Medical Records for Adaptive Risk Estimation](https://mlsanity.com/paper/2502.06124/) (2025)
- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
- [Sequential Diagnosis Prediction with Transformer and Ontological Representation](https://mlsanity.com/paper/2109.03069/) (2021)
- [Hierarchical Pretraining on Multimodal Electronic Health Records](https://mlsanity.com/paper/2310.07871/) (2023)
- [Graph-Text Multi-Modal Pre-training for Medical Representation Learning](https://mlsanity.com/paper/2203.09994/) (2022)
- [Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping](https://mlsanity.com/paper/2306.06994/) (2023)
- [A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts](https://mlsanity.com/paper/2101.10196/) (2021)
- [Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping](https://mlsanity.com/paper/2204.11291/) (2022)
- [ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations](https://mlsanity.com/paper/2207.01873/) (2022)
- [Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder](https://mlsanity.com/paper/2306.06579/) (2023)
- [Representation learning to advance multi-institutional studies with electronic health record data from US and France](https://mlsanity.com/paper/2502.08547/) (2025)
- [Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder](https://mlsanity.com/paper/2105.09428/) (2021)
- [Transformer-based unsupervised patient representation learning based on medical claims for risk stratification and analysis](https://mlsanity.com/paper/2106.12658/) (2021)
- [MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning](https://mlsanity.com/paper/2107.09288/) (2021)
- [Stock Embeddings: Learning Distributed Representations for Financial Assets](https://mlsanity.com/paper/2202.08968/) (2022)
- [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)
- [Predictive Modeling of Hospital Readmission: Challenges and Solutions](https://mlsanity.com/paper/2106.08488/) (2021)
- [sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion](https://mlsanity.com/paper/2211.17121/) (2022)
- [Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model](https://mlsanity.com/paper/2305.19997/) (2023)

## Abstract (arXiv)

Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification. Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits will have broad applications in healthcare analytics. However, in Electronic Health Records (EHR) the visit sequences of patients include multiple concepts (diagnosis, procedure, and medication codes) per visit. This structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within each visit. In this work, we propose Med2Vec, which not only learns distributed representations for both medical codes and visits from a large EHR dataset with over 3 million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec displays significant improvement in key medical applications compared to popular baselines such as Skip-gram, GloVe and stacked autoencoder, while providing clinically meaningful interpretation.