Description
This model detects the assertion status of menopause-related entities.
Predicted Entities
Present, Absent, Possible, Past, Hypothetical, Planned, Family, Menarche_Age, Menopause_Age
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_menopause_core", "en", "clinical/models")\
.setInputCols(["sentence", "token","embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
assertion = AssertionDLModel.pretrained("assertion_menopause_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 = ["""A 50-year-old woman, G2P1, presents with symptoms of perimenopause including night sweats, irregular menstruation, and fatigue.She has previously been diagnosed with hypertension. She is taking hormone replacement therapy with estradiol and norethindrone acetate. Recent tests included a bone density scan, which confirmed osteoporosis and showed elevated FSH levels. She also underwent a vaginal swab test for routine screening. Her mother has a history of breast cancer. Her menarche age was 11."""]
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_menopause_core", "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_menopause_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("""A 50-year-old woman, G2P1, presents with symptoms of perimenopause including night sweats, irregular menstruation, and fatigue.She has previously been diagnosed with hypertension. She is taking hormone replacement therapy with estradiol and norethindrone acetate. Recent tests included a bone density scan, which confirmed osteoporosis and showed elevated FSH levels. She also underwent a vaginal swab test for routine screening. Her mother has a history of breast cancer. Her menarche age was 11.""").toDF("text")
val result = pipeline.fit(sample_texts).transform(sample_texts)
Results
+---------------------------+-----+---+---------------------------+------------+----------+
|chunk |begin|end|ner_label |assertion |confidence|
+---------------------------+-----+---+---------------------------+------------+----------+
|G2P1 |21 |24 |G_P |Present |0.9999 |
|perimenopause |53 |65 |Perimenopause |Present |0.9999 |
|night sweats |77 |88 |Other_Symptom |Present |0.9997 |
|irregular menstruation |91 |112|Irregular_Menstruation |Present |0.9997 |
|fatigue |119 |125|Other_Symptom |Present |0.9954 |
|hypertension |166 |177|Hypertension |Past |0.9916 |
|hormone replacement therapy|194 |220|Hormone_Replacement_Therapy|Present |0.9988 |
|estradiol |227 |235|Hormone_Replacement_Therapy|Present |0.9696 |
|norethindrone acetate |241 |261|Hormone_Replacement_Therapy|Present |0.9984 |
|osteoporosis |323 |334|Osteoporosis |Present |1.0 |
|elevated |347 |354|Test_Result |Present |1.0 |
|FSH |356 |358|Hormone_Testing |Present |0.9999 |
|vaginal swab |389 |400|Vaginal_Swab |Present |1.0 |
|breast cancer |458 |470|Oncological |Family |0.9843 |
|11 |494 |495|Age |Menarche_Age|0.9891 |
+---------------------------+-----+---+---------------------------+------------+----------+
Model Information
| Model Name: | assertion_menopause_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 |
References
The datasets employed in training this model were meticulously curated and annotated using our in-house capabilities.
Benchmarking
label precision recall f1-score support
Absent 0.83 0.84 0.84 177
Family 0.47 0.73 0.57 11
Hypothetical 0.80 0.71 0.75 413
Menarche_Age 1.00 1.00 1.00 5
Menopause_Age 0.85 0.94 0.89 18
Past 0.46 0.41 0.43 44
Planned 0.80 0.67 0.73 6
Possible 0.59 0.46 0.52 28
Present 0.89 0.92 0.90 1558
accuracy - - 0.86 2260
macro-avg 0.74 0.74 0.74 2260
weighted-avg 0.86 0.86 0.86 2260