Description
This Generic Classifier model is intended for detecting tobacco use in clinical notes and trained by using GenericClassifierApproach annotator. Present: if the patient was a current consumer of tobacco. Past: the patient was a consumer in the past and had quit. Never: if the patient had never consumed tobacco. None: if there was no related text.
Predicted Entities
Present, Past, Never, None
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", 'en','clinical/models')\
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
features_asm = FeaturesAssembler()\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("features")
generic_classifier = GenericClassifierModel.pretrained("genericclassifier_sdoh_tobacco_usage_sbiobert_cased_mli", 'en', 'clinical/models')\
.setInputCols(["features"])\
.setOutputCol("class")
pipeline = Pipeline(stages=[
document_assembler,
sentence_embeddings,
features_asm,
generic_classifier
])
text_list = ["Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 25 yo daughter. He uses alcohol and cigarettes",
"The patient quit smoking approximately two years ago with an approximately a 40 pack year history, mostly cigar use. He also reports 'heavy alcohol use', quit 15 months ago.",
"The patient denies any history of smoking or alcohol abuse. She lives with her one daughter.",
"She was previously employed as a hairdresser, though says she hasnt worked in 4 years. Not reported by patient, but there is apparently a history of alochol abuse."]
df = spark.createDataFrame(text_list, StringType()).toDF("text")
result = pipeline.fit(df).transform(df)
result.select("text", "class.result").show(truncate=100)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")
.setInputCols("document")
.setOutputCol("sentence_embeddings")
val features_asm = new FeaturesAssembler()
.setInputCols("sentence_embeddings")
.setOutputCol("features")
val generic_classifier = GenericClassifierModel.pretrained("genericclassifier_sdoh_tobacco_usage_sbiobert_cased_mli", "en", "clinical/models")
.setInputCols("features")
.setOutputCol("class")
val pipeline = new PipelineModel().setStages(Array(
document_assembler,
sentence_embeddings,
features_asm,
generic_classifier))
val data = Seq("Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 25 yo daughter. He uses alcohol and cigarettes.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.generic.sdoh_tobacco_sbiobert_cased").predict("""The patient quit smoking approximately two years ago with an approximately a 40 pack year history, mostly cigar use. He also reports 'heavy alcohol use', quit 15 months ago.""")
Results
+----------------------------------------------------------------------------------------------------+---------+
| text| result|
+----------------------------------------------------------------------------------------------------+---------+
|Retired schoolteacher, now substitutes. Lives with wife in location 1439. Has a 27 yo son and a 2...|[Present]|
|The patient quit smoking approximately two years ago with an approximately a 40 pack year history...| [Past]|
| The patient denies any history of smoking or alcohol abuse. She lives with her one daughter.| [Never]|
|She was previously employed as a hairdresser, though says she hasnt worked in 4 years. Not report...| [None]|
+----------------------------------------------------------------------------------------------------+---------+
Model Information
| Model Name: | genericclassifier_sdoh_tobacco_usage_sbiobert_cased_mli |
| Compatibility: | Healthcare NLP 4.2.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [features] |
| Output Labels: | [prediction] |
| Language: | en |
| Size: | 3.4 MB |
Benchmarking
label precision recall f1-score support
Never 0.89 0.90 0.90 487
None 0.86 0.78 0.82 269
Past 0.87 0.79 0.83 415
Present 0.63 0.82 0.71 203
accuracy - - 0.83 1374
macro-avg 0.81 0.82 0.81 1374
weighted-avg 0.84 0.83 0.83 1374