It is a pretrained NER deep learning model for drug information relation extraction, and is trained on the 2018 i2b2 dataset (no FDA) with embeddings_clinical jsl model. The SparkNLP deep learning model (NerDL) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN, and is compatible with Spark NLP version 2.4.2.It detects drug strength, frequency, method of administration and dosage.
Input variables : Clinical text
Output Variables : Relation between drug, dosage, duration, frequency, strength
| 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 : colab.research.google.com
| Model Category | : | Commercial |
| Date Published | : | September, 2020 |
| Healthcare Domain | : |
Life Sciences
Payer Provider |
| Code | : | colab.research.google.com |
| Clinical Information Extraction |
| Clinical Trials |
| Drug Discovery |