Description
Entity Resolution model Based on KNN using Word Embeddings + Word Movers Distance.
Predicted Entities
ICD10-CM Codes and their normalized definition with clinical_embeddings.
Live Demo Open in Colab Copy S3 URI
How to use
...
neoplasm_resolver = ChunkEntityResolverModel.pretrained("chunkresolve_icd10cm_neoplasms_clinical","en","clinical/models")\
.setInputCols("token","chunk_embeddings")\
.setOutputCol("entity")
pipeline_puerile = Pipeline(stages = [documentAssembler, sentenceDetector, tokenizer, word_embeddings, clinical_ner, ner_converter, chunk_embeddings, neoplasm_resolver])
model = pipeline_puerile.fit(spark.createDataFrame([["""The patient is a 5-month-old infant who presented initially on Monday with a cold, cough, and runny nose for 2 days. Mom states she had no fever. Her appetite was good but she was spitting up a lot. She had no difficulty breathing and her cough was described as dry and hacky. At that time, physical exam showed a right TM, which was red. Left TM was okay. She was fairly congested but looked happy and playful. She was started on Amoxil and Aldex and we told to recheck in 2 weeks to recheck her ear. Mom returned to clinic again today because she got much worse overnight. She was having difficulty breathing. She was much more congested and her appetite had decreased significantly today. She also spiked a temperature yesterday of 102.6 and always having trouble sleeping secondary to congestion."""]]).toDF("text"))
results = model.transform(data)
...
val neoplasm_resolver = ChunkEntityResolverModel.pretrained("chunkresolve_icd10cm_neoplasms_clinical","en","clinical/models")
.setInputCols(Array("token","chunk_embeddings"))
.setOutputCol("resolution")
val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, word_embeddings, clinical_ner, ner_converter, chunk_embeddings, neoplasm_resolver))
val data = Seq("The patient is a 5-month-old infant who presented initially on Monday with a cold, cough, and runny nose for 2 days. Mom states she had no fever. Her appetite was good but she was spitting up a lot. She had no difficulty breathing and her cough was described as dry and hacky. At that time, physical exam showed a right TM, which was red. Left TM was okay. She was fairly congested but looked happy and playful. She was started on Amoxil and Aldex and we told to recheck in 2 weeks to recheck her ear. Mom returned to clinic again today because she got much worse overnight. She was having difficulty breathing. She was much more congested and her appetite had decreased significantly today. She also spiked a temperature yesterday of 102.6 and always having trouble sleeping secondary to congestion.").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
chunk entity icd10_neoplasm_description icd10_neoplasm_code
0 patient Organism Acute myelomonocytic leukemia, in remission C9251
1 infant Organism Malignant (primary) neoplasm, unspecified C801
2 nose Organ Malignant neoplasm of nasal cavity C300
3 She Organism Malignant neoplasm of thyroid gland C73
4 She Organism Malignant neoplasm of thyroid gland C73
5 She Organism Malignant neoplasm of thyroid gland C73
6 Aldex Gene_or_gene_product Acute megakaryoblastic leukemia not having ach... C9420
7 ear Organ Other benign neoplasm of skin of right ear and... D2321
8 She Organism Malignant neoplasm of thyroid gland C73
9 She Organism Malignant neoplasm of thyroid gland C73
10 She Organism Malignant neoplasm of thyroid gland C73
Model Information
| Name: | chunkresolve_icd10cm_neoplasms_clinical | |
| Type: | ChunkEntityResolverModel | |
| Compatibility: | Spark NLP 2.4.5+ | |
| License: | Licensed | |
| Edition: | Official | |
| Input labels: | [token, chunk_embeddings] | |
| Output labels: | [entity] | |
| Language: | en | |
| Case sensitive: | True | |
| Dependencies: | embeddings_clinical |
Data Source
Trained on ICD10CM Dataset Ranges: C000-D489, R590-R599 https://www.icd10data.com/ICD10CM/Codes/C00-D49