This model maps extracted medical entities to RxNorm codes using chunk embeddings, and has faster load time, with a speedup of about 6X when compared to previous versions. Also the load process now is more memory friendly meaning that the maximum memory required during load time is smaller, reducing the chances of OOM exceptions, and thus relaxing hardware requirements. Model is compatible with Spark NLP for Healthcare version 3.0.4 onwards.
Input variables : Embedding, named entitity recognition chunks
Output Variables : RxNorm Codes and their normalized definition
| 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 : nlp.johnsnowlabs.com
| Model Category | : | Commercial |
| Date Published | : | May, 2021 |
| Healthcare Domain | : |
Payer
Provider |
| Code | : | colab.research.google.com |
| Clinical Information Extraction |