Description
Entity Resolution model Based on KNN using Word Embeddings + Word Movers Distance.
Predicted Entities
CPT Codes and their normalized definition with clinical_embeddings.
How to use
...
cpt_resolver = ChunkEntityResolverModel.pretrained("chunkresolve_cpt_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, cpt_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 cpt_resolver = ChunkEntityResolverModel.pretrained("chunkresolve_cpt_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, cpt_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 cpt_description cpt_code
0 a cold, cough PROBLEM Thoracoscopy, surgical; with removal of a sing... 32669
1 runny nose PROBLEM Unlisted procedure, larynx 31599
2 fever PROBLEM Cesarean delivery only; 59514
3 difficulty breathing PROBLEM Repair, laceration of diaphragm, any approach 39501
4 her cough PROBLEM Exploration for postoperative hemorrhage, thro... 35840
5 physical exam TEST Cesarean delivery only; including postpartum care 59515
6 fairly congested PROBLEM Pyelotomy; with drainage, pyelostomy 50125
7 Amoxil TREATMENT Cholecystoenterostomy; with gastroenterostomy 47721
8 Aldex TREATMENT Laparoscopy, surgical; with omentopexy (omenta... 49326
9 difficulty breathing PROBLEM Repair, laceration of diaphragm, any approach 39501
10 more congested PROBLEM for section of 1 or more cranial nerves 61460
11 trouble sleeping PROBLEM Repair, laceration of diaphragm, any approach 39501
12 congestion PROBLEM Repair, laceration of diaphragm, any approach 39501
Model Information
| Name: | chunkresolve_cpt_clinical | |
| Type: | ChunkEntityResolverModel | |
| Compatibility: | Spark NLP 2.4.2+ | |
| License: | Licensed | |
| Edition: | Official | |
| Input labels: | token, chunk_embeddings | |
| Output labels: | entity | |
| Language: | en | |
| Case sensitive: | True | |
| Dependencies: | embeddings_clinical |
Data Source
Trained on Current Procedural Terminology dataset.