EHR is a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes. Patient2Vec gives a framework to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient. The framework uses deep recurrent neural networks to capture the complex relationships between clinical events in the patient’s EHR data and employs the attention mechanism to learn a personalized representation and to obtain relative feature importance. To evaluate this approach, it was applied to the prediction of future hospitalizations using real EHR data and its predictive performance was compared 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.
Input variables : EHR data sequences
Output Variables : EHR data patient characteristics
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | October, 2018 |
| Healthcare Domain | : | Payer |
| Code | : | github.com |
| Clinical Information Extraction |
| Disease Clinical Diagnosis and Treatment |