It is a pretrained NER deep learning model for detecting lab test names, their findings, measurements, results, and date, i.e. it identifies “is_finding_of”(A is a finding of B), “is_result_of”(A was caused by B), “is_date_of”(A occurred on date C), “Outside of an Occurrence” tags. It is compatible with Spark NLP for Healthcare versions 2.7.4+ and is used as a part of an nlp pipeline with the following stages: DocumentAssembler, SentenceDetector, Tokenizer, PerceptronModel, DependencyParserModel, WordEmbeddingsModel, NerDLModel, NerConverter, RelationExtractionModel. The model is trained on internal JSL data and has an overall precision of 87.25%.
Input variables : Clinical text
Output Variables : Relation between lab test names, their findings, measurements, results, and datee.g. is_finding_of, is_result_of, is_date_of, O.
| 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 | : | February, 2021 |
| Healthcare Domain | : |
Payer
Provider |
| Code | : | nlp.johnsnowlabs.com |
| Patient Centric Care |
| Precision Medicine |