Description
This model maps extracted medical entities to ICD10-GM codes for the German language using sent_bert_base_cased (de) embeddings.
Predicted Entities
ICD10GM Codes
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("ner_chunk")
sbert_embedder = BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "de")\
.setInputCols(["ner_chunk"])\
.setOutputCol("sbert_embeddings")
icd10gm_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_icd10gm", "de", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("icd10gm_code")
icd10gm_pipelineModel = PipelineModel(
stages = [
documentAssembler,
sbert_embedder,
icd10gm_resolver])
icd_lp = LightPipeline(icd10gm_pipelineModel)
val documentAssembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("ner_chunk")
val sbert_embedder = BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "de")
.setInputCols("ner_chunk")
.setOutputCol("sbert_embeddings")
val icd10gm_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_icd10gm", "de", "clinical/models") \
.setInputCols(["sbert_embeddings"]) \
.setOutputCol("icd10gm_code")
val icd10gm_pipelineModel = new PipelineModel().setStages(Array(documentAssembler,sbert_embedder,icd10gm_resolver))
val icd_lp = LightPipeline(icd10gm_pipelineModel)
import nlu
nlu.load("de.resolve.icd10gm").predict("""Put your text here.""")
Results
| | chunks | code | resolutions | all_codes | all_distances |
|---:|:--------|:--------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | Dyspnoe | C671 |Dyspnoe, Schlafapnoe, Dysphonie, Frühsyphilis, Hyperzementose, Hypertrichose, Makrostomie, Dystonie, Nokardiose, Lebersklerose, Dyspareunie, Schizophrenie, Skoliose, Dysurie, Diphyllobothriose, Heterophorie, Rektozele, Enophthalmus, Amyloidose, Hyperventilation, Neurasthenie, Sarkoidose, Psoriasis-Arthropathie, Hyperodontie, Enteroptose| [R06.0, G47.3, R49.0, A51, K03.4, L68, Q18.4, G24, A43, K74.1, N94.1, F20, M41, R30.0, B70.0, H50.5, N81.6, H05.4, E85, R06.4, F48.0, D86, L40.5, K00.1, K63.4] | [0.0000, 2.5602, 3.0529, 3.3310, 3.4645, 3.7148, 3.7568, 3.8115, 3.8557, 3.8577, 3.9448, 3.9681, 3.9799, 3.9889, 4.0036, 4.0773, 4.0825, 4.1342, 4.2031, 4.2155, 4.2313, 4.2341, 4.2775, 4.2802, 4.2823] |
Model Information
| Model Name: | sbertresolve_icd10gm |
| Compatibility: | Healthcare NLP 3.2.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [icd10_gm2022_de_code] |
| Language: | de |
| Case sensitive: | false |