Skip to content
Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
LICENSE Create LICENSE Oct 18, 2018
Patient2Vec.png Add files via upload Oct 18, 2018
Patient2Vec.py Add files via upload Oct 18, 2018
README.rst Update README.rst Nov 27, 2018

README.rst

DOI arxiv license twitter

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

Referenced paper : Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

Patient2Vec

Documentation

The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in this data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure and it achieves an AUC around 0.799 outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights.

Citation:

@ARTICLE{Patient2Vec,
      author={Zhang, Jinghe and Kowsari, Kamran and Harrison, James H and Lobo, Jennifer M and Barnes, Laura E},
      journal={IEEE Access},
      title={Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record},
      year={2018},
      volume={6},
      pages={65333-65346},
      doi={10.1109/ACCESS.2018.2875677},
      ISSN={2169-3536}
}
You can’t perform that action at this time.