Description
This model is designed to map extracted phenotype entities from clinical or biomedical text to their corresponding Human Phenotype Ontology (HPO) codes. It ensures that observed symptoms, signs, and clinical abnormalities are standardized using HPO terminology.
Predicted Entities
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
stopwords_cleaner = StopWordsCleaner()\
.setInputCols("token")\
.setOutputCol("cleanTokens")\
.setCaseSensitive(False)
token_assembler = TokenAssembler()\
.setInputCols(['document',"cleanTokens"])\
.setOutputCol("cleanTokens_newDoc")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models") \
.setInputCols(["cleanTokens_newDoc"]) \
.setOutputCol("sentence")
tokenizer_2 = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("clean_tokens")
entityExtractor = TextMatcherInternalModel().pretrained("hpo_matcher","en", "clinical/models")\
.setInputCols(["sentence", "clean_tokens"])\
.setOutputCol("hpo_term")\
.setCaseSensitive(False)\
.setMergeOverlapping(False)
mapper = ChunkMapperModel().pretrained("hpo_mapper","en", "clinical/models")\
.setInputCols(["hpo_term"])\
.setOutputCol("hpo_code")\
.setLowerCase(True)
mapper_pipeline = Pipeline().setStages([
documentAssembler,
tokenizer,
stopwords_cleaner,
token_assembler,
sentenceDetector,
tokenizer_2,
entityExtractor,
mapper])
text = ''' APNEA: Presumed apnea of prematurity since < 34 wks gestation at birth.
HYPERBILIRUBINEMIA: At risk for hyperbilirubinemia d/t prematurity.
1/25-1/30: Received Amp/Gent while undergoing sepsis evaluation. '''
data = spark.createDataFrame([[text]]).toDF("text")
mapper_model = mapper_pipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = nlp.Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
stopwords_cleaner = nlp.StopWordsCleaner()\
.setInputCols("token")\
.setOutputCol("cleanTokens")\
.setCaseSensitive(False)
token_assembler = nlp.TokenAssembler()\
.setInputCols(['document',"cleanTokens"])\
.setOutputCol("cleanTokens_newDoc")
sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models") \
.setInputCols(["cleanTokens_newDoc"]) \
.setOutputCol("sentence")
tokenizer_2 = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("clean_tokens")
entityExtractor = medical.TextMatcherInternalModel().pretrained("hpo_matcher","en", "clinical/models")\
.setInputCols(["sentence", "clean_tokens"])\
.setOutputCol("hpo_term")\
.setCaseSensitive(False)\
.setMergeOverlapping(False)
mapper = medical.ChunkMapperModel().pretrained("hpo_mapper","en", "clinical/models")\
.setInputCols(["hpo_term"])\
.setOutputCol("hpo_code")\
.setLowerCase(True)
mapper_pipeline = nlp.Pipeline().setStages([
documentAssembler,
tokenizer,
stopwords_cleaner,
token_assembler,
sentenceDetector,
tokenizer_2,
entityExtractor,
mapper])
text = ''' APNEA: Presumed apnea of prematurity since < 34 wks gestation at birth.
HYPERBILIRUBINEMIA: At risk for hyperbilirubinemia d/t prematurity.
1/25-1/30: Received Amp/Gent while undergoing sepsis evaluation. '''
data = spark.createDataFrame([[text]]).toDF("text")
mapper_model = mapper_pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val stopwordsCleaner = new StopWordsCleaner()
.setInputCols("token")
.setOutputCol("cleanTokens")
.setCaseSensitive(false)
val tokenAssembler = new TokenAssembler()
.setInputCols(Array("document", "cleanTokens"))
.setOutputCol("cleanTokens_newDoc")
val sentenceDetector = SentenceDetectorDLModel
.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.setInputCols(Array("cleanTokens_newDoc"))
.setOutputCol("sentence")
val tokenizer_2 = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("clean_tokens")
val entityExtractor = TextMatcherInternalModel
.pretrained("hpo_matcher", "en", "clinical/models")
.setInputCols(Array("sentence", "clean_tokens"))
.setOutputCol("hpo_term")
.setCaseSensitive(false)
.setMergeOverlapping(false)
val mapper = medical.ChunkMapperModel
.pretrained("hpo_mapper", "en", "clinical/models")
.setInputCols("hpo_term")
.setOutputCol("hpo_code")
.setLowerCase(True)
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
tokenizer,
stopwordsCleaner,
tokenAssembler,
sentenceDetector,
tokenizer_2,
entityExtractor,
mapper
))
val data = Seq("""APNEA: Presumed apnea of prematurity since < 34 wks gestation at birth.
GENETICS: Holds thumbs in palms, findings on Hemolytic Uremic Syndrome, history of meconium plugs.
HYPERBILIRUBINEMIA: At risk for hyperbilirubinemia d/t prematurity.
1/25-1/30: Received Amp/Gent while undergoing sepsis evaluation.""").toDF("text")
val mapper = pipeline.fit(data).transform(data)
Results
+------------------+-----+---+-----+----------+
| chunk|begin|end|label| hpo_code|
+------------------+-----+---+-----+----------+
| APNEA| 0| 4| HPO|HP:0002104|
| apnea| 16| 20| HPO|HP:0002104|
|HYPERBILIRUBINEMIA| 66| 83| HPO|HP:0002904|
|hyperbilirubinemia| 91|108| HPO|HP:0002904|
| sepsis| 167|172| HPO|HP:0100806|
+------------------+-----+---+-----+----------+
Model Information
| Model Name: | hpo_mapper |
| Compatibility: | Healthcare NLP 6.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [hpo_code] |
| Language: | en |
| Size: | 1.5 MB |