It is a pretrained NER DL model for clinical terminology/entities. The SparkNLP DL model (MedicalNerModel) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN. It is compatible with Spark NLP for Healthcare versions 3.0.0+, and has an macro f1 score of 0.815, while a micro f1 score of 0.879.
Input variables : Clinical text
Output Variables : Injury_or_Poisoning, Direction, Test, Admission_Discharge, Death_Entity, Relationship_Status, Duration
| 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 | : | March, 2021 |
| Healthcare Domain | : |
Payer
Provider |
| Code | : | colab.research.google.com |
| Clinical Information Extraction |