Description
This model classifies related to social economics status in the clinical documents and trained by using GenericClassifierApproach annotator. True: if the patient was currently employed or unemployed. False: if there was no related passage.
Predicted Entities
True, False
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_economics_binary_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."]
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_economics_binary_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_ecnomics_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...| [True]|
|The patient quit smoking approximately two years ago with an approximately a 40 pack year history...|[False]|
+----------------------------------------------------------------------------------------------------+-------+
Model Information
| Model Name: | genericclassifier_sdoh_economics_binary_sbiobert_cased_mli |
| Compatibility: | Healthcare NLP 4.2.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [features] |
| Output Labels: | [prediction] |
| Language: | en |
| Size: | 3.5 MB |
Benchmarking
label precision recall f1-score support
False 0.93 0.85 0.89 894
True 0.79 0.90 0.84 562
accuracy - - 0.87 1456
macro-avg 0.86 0.87 0.86 1456
weighted-avg 0.87 0.87 0.87 1456