Description
This pipeline extracts clinical findings and maps them to their corresponding SNOMED (CT version) codes using sbiobert_base_cased_mli Sentence Bert Embeddings.
How to use
from sparknlp.pretrained import PretrainedPipeline
snomed_pipeline = PretrainedPipeline("snomed_findings_resolver_pipeline", "en", "clinical/models")
result = snomed_pipeline.annotate("""The patient exhibited recurrent upper respiratory tract infections, fever, unintentional weight loss, and occasional night sweats. Clinically, they appeared cachectic and pale, with notable hepatosplenomegaly. Laboratory results confirmed pancytopenia.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val snomed_pipeline = PretrainedPipeline("snomed_findings_resolver_pipeline", "en", "clinical/models")
val result = snomed_pipeline.annotate("""The patient exhibited recurrent upper respiratory tract infections, fever, unintentional weight loss, and occasional night sweats. Clinically, they appeared cachectic and pale, with notable hepatosplenomegaly. Laboratory results confirmed pancytopenia.""")
Results
+----------------------------------+-----+---+---------+-----------+-------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
| chunk|begin|end|ner_label|snomed_code| resolution| all_k_resolutions| all_k_codes|
+----------------------------------+-----+---+---------+-----------+-------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
|upper respiratory tract infections| 32| 65| PROBLEM| 195708003|recurrent upper respiratory tract infection|recurrent upper respiratory tract infection:::upper respi...|195708003:::54150009:::312118003:::448739000:::4519910001...|
| fever| 68| 72| PROBLEM| 386661006| fever|fever:::intermittent fever:::sustained fever:::prolonged ...|386661006:::77957000:::271751000:::248435007:::12579009::...|
| unintentional weight loss| 75| 99| PROBLEM| 448765001| unintentional weight loss|unintentional weight loss:::unexplained weight loss:::int...|448765001:::422868009:::416528001:::267024001:::89362005:...|
| night sweats| 117|128| PROBLEM| 42984000| night sweats|night sweats:::frequent night waking:::night waking:::nig...|42984000:::423052008:::67233009:::102549009:::36163009:::...|
| cachectic| 157|165| PROBLEM| 238108007| cachectic|cachectic:::cachexia associated with aids:::cardiac cache...|238108007:::422003001:::284529003:::788876001:::240128005...|
| pale| 171|174| PROBLEM| 398979000| pale complexion|pale complexion:::pale liver:::pale tongue:::pale lung:::...|398979000:::95199009:::719637000:::95200007:::70396004:::...|
| hepatosplenomegaly| 190|207| PROBLEM| 36760000| hepatosplenomegaly|hepatosplenomegaly:::congestive splenomegaly:::neonatal h...|36760000:::19058002:::80378000:::16294009:::191382009:::8...|
| pancytopenia| 239|250| PROBLEM| 127034005| pancytopenia|pancytopenia:::drug induced pancytopenia:::pancytopenia -...|127034005:::736024007:::5876000:::124961001:::417672002::...|
+----------------------------------+-----+---+---------+-----------+-------------------------------------------+------------------------------------------------------------+------------------------------------------------------------+
Model Information
| Model Name: | snomed_findings_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 2.8 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMergeModel
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel