It’s a common task in healthcare to extract different entities from the medical records.This is a pretrained NER DL model for clinical terminology. This operation allows us to convert raw data to the structured, which can be analyzed with statistical approaches or displayed in the reports. By providing medical note as an input to the model, one can get demographics and clinical entities like lab name, lab result, symptomps etc. 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, 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.
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 |