Model predicts severity score for COVID-19 pneumonia for frontal chest X-ray images. The ability to gauge severity of COVID-19 lung infections can be used for escalation or de-escalation of care, especially in the ICU. A neural network model that was pre-trained on large(non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for this task. An automated tool can be used on patients over time to track disease progression and treatment response.
Input variables : Chest X-ray images
Output Variables : Severity score for COVID-19 pneumonia
| 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 |
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters | Optimized Hospital Resource Utilization | Decreased Cost of Care | Decreased Patient Visits |
| 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 | : | May, 2020 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Medical Imaging |
| Health Risk Management |
| Health Risk Prediction |