# Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding

arXiv: https://arxiv.org/abs/1902.03376 · HTML: https://mlsanity.com/paper/1902.03376/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2019.

## Built on by (most cited first, 12 of 12)

- [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)
- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes](https://mlsanity.com/paper/2111.06152/) (2021)
- [Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models](https://mlsanity.com/paper/2405.16413/) (2024)
- [Toward Cohort Intelligence: A Universal Cohort Representation Learning Framework for Electronic Health Record Analysis](https://mlsanity.com/paper/2304.04468/) (2023)
- [Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction](https://mlsanity.com/paper/2308.12575/) (2023)
- [SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding](https://mlsanity.com/paper/2202.01427/) (2022)
- [Modeling electronic health record data using a knowledge-graph-embedded topic model](https://mlsanity.com/paper/2206.01436/) (2022)
- [Dynamic Healthcare Embeddings for Improving Patient Care](https://mlsanity.com/paper/2303.11563/) (2023)
- [Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data](https://mlsanity.com/paper/2506.07092/) (2025)
- [Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support](https://mlsanity.com/paper/2610.06355/) (2026)

## Abstract (arXiv)

Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity research is Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, and sparse. Though existing studies on learning patient similarity from EHRs have shown being useful in solving real clinical problems, their applicability is limited due to the lack of medical interpretations. Moreover, most previous methods assume a vector-based representation for patients, which typically requires aggregation of medical events over a certain time period. As a consequence, temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based on the temporal matching of longitudinal patient EHRs. Two efficient methods are presented, unsupervised and supervised, both of which preserve the temporal properties in EHRs. The supervised scheme takes a convolutional neural network architecture and learns an optimal representation of patient clinical records with medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement over the baselines. We make our code and sample data available for further study.