Adverse reactions of marketed and approved drugs cause a significant amount of morbidity and mortality. To associate these drugs with human adverse drug reactions (ADRs), FDA Adverse Event Reports are utilized and developed random forest models that predict ADR occurrences from in vitro pharmacological profiles.Through target-centric approach, identified 221 statistical associations between protein targets and adverse reactions, which provide novel insight into the molecular components underlying physiological adverse reactions.
Input variables : AC50 values for 184 assays
Output Variables : ADR occurrences
| 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 | : | # of Adverse Events |
| 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 : thelancet.com
Additional links : thelancet.com
| Model Category | : | Public |
| Date Published | : | July, 2020 |
| Healthcare Domain | : | Life Sciences |
| Code | : | github.com |
| Clinical Trials |
| Adverse Drug Events |