It is a pretrained NER deep learning model for clinical terminology and detects Diagnosis, Symptoms, Drugs, Labs and Demographics data. The SparkNLP deep learning model (MedicalNerModel) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN and is also available with BioBERT. It is compatible with Spark NLP for Healthcare versions 3.0.0+, and is trained on data gathered and manually annotated by John Snow Labs. The macro averaged f1 score for the model is 59%, while the micro averaged f1 score is 71.9%.
Input variables : Clinical text
Output Variables : Age, Diagnosis, Dosage, Drug_Name, Frequency, Gender, Lab_Name, Lab_Result, Symptom_Name.
| 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 |