Electronic medical records that have been anonymized are becoming a more popular source of research data. However, these datasets frequently lack information on race and ethnicity. This causes difficulties for researchers modelling human disease because race and ethnicity are powerful confounders for many health exposures and treatment outcomes; race and ethnicity are also closely linked to population-specific genetic variation. The model uses deep neural networks to generate more accurate estimates for missing racial and ethnic information than competing methods
Input variables : Electronic medical records
Output Variables : Imputed Race and Ethinicity
| 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 | : | April, 2018 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Data Privacy |
| Synthetic Data Generation |