Named Entity recognition annotator allows for a generic model to be trained by utilizing a deep learning algorithm (Char CNNs - BiLSTM - CRF - word embeddings) inspired on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM, CNN. De-identification NER (Enriched) is a Named Entity Recognition model that annotates text to find protected health information that may need to be deidentified. The entities it annotates are Age, City, Country, Date, Doctor, Hospital, Idnum, Medicalrecord, Organization, Patient, Phone, Profession, State, Street, Username, and Zip. Clinical NER is trained with the ‘embeddings_clinical’ word embeddings model, so be sure to use the same embeddings in the pipeline. It is compatible with Spark NLP for Healthcare versions 3.0.0+, and is trained on JSL enriched n2c2 2014: De-identification and Heart Disease Risk Factors Challenge datasets with embeddings_clinical jsl model
Input variables : Clinical text
Output Variables : Age, City, Country, Date, Doctor, Hospital, Idnum, Medical record, Organization, Patient, Phone, Profession, State, Street, Username, Zip
| 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 |
| Data Privacy |
| Deidentification |