It is a Pretrained NER deep learning model for posology (used for detecting drug Information) 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. The output is a dataframe with a sentence per row and a "ner" column containing all of the entity labels in the sentence, entity character indices, and other metadata. To get only the tokens and entity labels, without the metadata, select "token.result" and "ner.result" from your output dataframe or add the "Finisher" to the end of your pipeline.
Input variables : Clinical text
Output Variables : DOSAGE, DRUG, DURATION, FORM, FREQUENCY, ROUTE, 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 : nlp.johnsnowlabs.com
| Model Category | : | Commercial |
| Date Published | : | March, 2021 |
| Healthcare Domain | : |
Life Sciences
Payer Provider |
| Code | : | colab.research.google.com |
| Clinical Information Extraction |
| Clinical Trials |
| Drug Discovery |