Description
This model detects the assertion status of entities related to alcohol-smoking.
Predicted Entities
Absent, Hypothetical_Possible, Past_History, Present_Planned
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
ner_model = MedicalNerModel.pretrained("ner_alcohol_smoking", "en", "clinical/models")\
.setInputCols(["sentence", "token","embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
assertion = AssertionDLModel.pretrained("assertion_alcohol_smoking_wip", "en", "clinical/models")\
.setInputCols(["sentence", "ner_chunk", "embeddings"]) \
.setOutputCol("assertion")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
clinical_embeddings,
ner_model,
ner_converter,
assertion
])
sample_texts = ["""Per the patient, the last drink was on ___, prior to admission. The patient admits to having experienced tremors, palpitations, and diaphoresis during the past alcohol withdrawals, but he denies ever having experienced seizures. Mr. ___ did not report experiencing any symptoms of withdrawal throughout his hospital stay, and an examination revealed no evidence of withdrawal.""",
"""SUBSTANCE ABUSE: The patient admitted to occasional binge drinking, but admitted to normally consuming one pint of liquor a day in the week before her admission. Before she attempted suicide, she was heavily intoxicated and had a high blood alcohol level (BAL). Attending the AA meetings and expressing a desire to keep going to AA to support sobriety were two ways the patient showed motivation to stop drinking. The patient was put on the CIWA protocol upon admission, but no PRN Valium was needed for alcohol withdrawal."""]
data = spark.createDataFrame(sample_texts, StringType()).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "en")
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val clinical_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner_model = MedicalNerModel.pretrained("ner_alcohol_smoking", "en", "clinical/models")
.setInputCols(Array("sentence", "token","embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val assertion = AssertionDLModel.pretrained("assertion_alcohol_smoking_wip", "en", "clinical/models")
.setInputCols(Array("sentence", "ner_chunk", "embeddings"))
.setOutputCol("assertion")
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
clinical_embeddings,
ner_model,
ner_converter,
assertion
))
val sample_texts = Seq("""Per the patient, the last drink was on ___, prior to admission. The patient admits to having experienced tremors, palpitations, and diaphoresis during the past alcohol withdrawals, but he denies ever having experienced seizures. Mr. ___ did not report experiencing any symptoms of withdrawal throughout his hospital stay, and an examination revealed no evidence of withdrawal.""",
"""SUBSTANCE ABUSE: The patient admitted to occasional binge drinking, but admitted to normally consuming one pint of liquor a day in the week before her admission. Before she attempted suicide, she was heavily intoxicated and had a high blood alcohol level (BAL). Attending the AA meetings and expressing a desire to keep going to AA to support sobriety were two ways the patient showed motivation to stop drinking. The patient was put on the CIWA protocol upon admission, but no PRN Valium was needed for alcohol withdrawal.""").toDF("text")
val result = pipeline.fit(sample_texts).transform(sample_texts)
Results
+------------+-----+---+----------------------+---------------+----------+
|chunk |begin|end|ner_label |assertion |confidence|
+------------+-----+---+----------------------+---------------+----------+
|drink |26 |30 |Drinking_Status |Past_History |0.8507 |
|tremors |105 |111|Psychoneurologic_Issue|Past_History |0.9315 |
|palpitations|114 |125|Cardiovascular_Issues |Past_History |0.9251 |
|diaphoresis |132 |142|Other_Health_Issues |Past_History |0.9181 |
|alcohol |160 |166|Drinking_Status |Past_History |0.9109 |
|seizures |219 |226|Psychoneurologic_Issue|Absent |0.5359 |
|binge |52 |56 |Substance_Quantity |Present_Planned|0.5528 |
|drinking |58 |65 |Drinking_Status |Present_Planned|0.5704 |
|one pint |103 |110|Substance_Quantity |Present_Planned|0.6838 |
|liquor |115 |120|Alcohol_Type |Present_Planned|0.6879 |
|a day |122 |126|Substance_Frequency |Present_Planned|0.8029 |
|suicide |183 |189|Psychoneurologic_Issue|Past_History |0.731 |
|intoxicated |208 |218|Psychoneurologic_Issue|Past_History |0.7832 |
|alcohol |241 |247|Drinking_Status |Past_History |0.507 |
|AA |276 |277|Cessation_Treatment |Present_Planned|0.4559 |
|AA |329 |330|Cessation_Treatment |Present_Planned|0.5112 |
|drinking |404 |411|Drinking_Status |Present_Planned|0.5385 |
|CIWA |441 |444|Withdrawal_Treatment |Present_Planned|0.5693 |
|Valium |482 |487|Withdrawal_Treatment |Absent |0.553 |
|alcohol |504 |510|Drinking_Status |Present_Planned|0.5135 |
+------------+-----+---+----------------------+---------------+----------+
Model Information
| Model Name: | assertion_alcohol_smoking_wip |
| Compatibility: | Healthcare NLP 5.3.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, ner_chunk, embeddings] |
| Output Labels: | [assertion_pred] |
| Language: | en |
| Size: | 2.5 MB |
Benchmarking
label precision recall f1-score support
Absent 0.84 0.74 0.79 213
Hypothetical_Possible 0.67 0.81 0.74 115
Past_History 0.77 0.72 0.75 221
Present_Planned 0.74 0.78 0.76 327
accuracy - - 0.76 876
macro-avg 0.76 0.76 0.76 876
weighted-avg 0.76 0.76 0.76 876