Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer – impaired learning if tasks are not appropriately selected. In this paper, they introduced a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them.
Input variables : EHR Records, MIMIC-III Dataset
Output Variables : Acute Kidney Injury, Continuous Renal Replacement Therapy, Mechanical Ventilation, Vasoactive Medications, Mortality, and Length of Stay
| 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 : academic.oup.com
| Model Category | : | Public |
| Date Published | : | May, 2021 |
| Healthcare Domain | : |
Payer
Provider |
| Code | : | github.com |
| Population Health |