Description
The Oncological Response to Treatment classifier employs MedicalBertForSequenceClassification embeddings within a robust classifier architecture. Trained on a diverse dataset, this model provides accurate label assignments and confidence scores for its predictions. The primary goal of this model is to categorize text into two key labels: Yes and No.
-
Yes: The patient responded to treatment. -
No: The patient did not respond to treatment.
Predicted Entities
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_response_to_treatment", "en", "clinical/models")\
.setInputCols(["document","token"])\
.setOutputCol("prediction")
pipeline = Pipeline(
stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
sample_texts = [
["Contrast-enhanced MRI of the brain showed no change in the size of the glioblastoma, suggesting stable disease post-temozolomide therapy."],
["The breast ultrasound after neoadjuvant chemotherapy displayed a decrease in the primary lesion size from 3 cm to 1 cm, suggesting a favorable response to treatment. The skin infection is also well controlled with multi-antibiotic approach. "],
["MRI of the pelvis indicated no further progression of endometriosis after laparoscopic excision and six months of hormonal suppression therapy."],
["A repeat endoscopy revealed healing gastric ulcers with new signs of malignancy or H. pylori infection. Will discuss the PPI continuum."],
["Dynamic contrast-enhanced MRI of the liver revealed no significant reduction in the size and number of hepatic metastases following six months of targeted therapy with sorafenib."],
["Digital subtraction angiography of the cerebral vessels displayed further aneurysmal dilation and new vascular abnormalities after endovascular coiling of a cerebral aneurysm, indicating a unsuccessful intervention."],
["The patient's repeat spirometry tests demonstrated non-significant improvement in both FEV1 and FVC, suggesting ineffective control of asthma symptoms with even maximally optimized inhaler therapy. Continuum will discuss."]
]
sample_data = spark.createDataFrame(sample_texts).toDF("text")
result = pipeline.fit(sample_data).transform(sample_data)
result.select("text", "prediction.result").show(truncate=100)
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_response_to_treatment", "en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("prediction")
val pipeline = new Pipeline().setStages(Array(
documenter,
tokenizer,
sequenceClassifier))
val data = Seq(Array(
"Contrast-enhanced MRI of the brain showed no change in the size of the glioblastoma, suggesting stable disease post-temozolomide therapy.",
"The breast ultrasound after neoadjuvant chemotherapy displayed a decrease in the primary lesion size from 3 cm to 1 cm, suggesting a favorable response to treatment. The skin infection is also well controlled with multi-antibiotic approach. ",
"MRI of the pelvis indicated no further progression of endometriosis after laparoscopic excision and six months of hormonal suppression therapy.",
"A repeat endoscopy revealed healing gastric ulcers with new signs of malignancy or H. pylori infection. Will discuss the PPI continuum.",
"Dynamic contrast-enhanced MRI of the liver revealed no significant reduction in the size and number of hepatic metastases following six months of targeted therapy with sorafenib.",
"Digital subtraction angiography of the cerebral vessels displayed further aneurysmal dilation and new vascular abnormalities after endovascular coiling of a cerebral aneurysm, indicating a unsuccessful intervention.",
"The patient's repeat spirometry tests demonstrated non-significant improvement in both FEV1 and FVC, suggesting ineffective control of asthma symptoms with even maximally optimized inhaler therapy. Continuum will discuss."
)).toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+----------------------------------------------------------------------------------------------------+------+
| text|result|
+----------------------------------------------------------------------------------------------------+------+
|Contrast-enhanced MRI of the brain showed no change in the size of the glioblastoma, suggesting s...| [Yes]|
|The breast ultrasound after neoadjuvant chemotherapy displayed a decrease in the primary lesion s...| [Yes]|
|MRI of the pelvis indicated no further progression of endometriosis after laparoscopic excision a...| [Yes]|
|A repeat endoscopy revealed healing gastric ulcers with new signs of malignancy or H. pylori infe...| [No]|
|Dynamic contrast-enhanced MRI of the liver revealed no significant reduction in the size and numb...| [No]|
|Digital subtraction angiography of the cerebral vessels displayed further aneurysmal dilation an...| [No]|
|The patient's repeat spirometry tests demonstrated non-significant improvement in both FEV1 and F...| [No]|
+----------------------------------------------------------------------------------------------------+------+
Model Information
| Model Name: | bert_sequence_classifier_response_to_treatment |
| Compatibility: | Healthcare NLP 5.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [prediction] |
| Language: | en |
| Size: | 406.6 MB |
| Case sensitive: | false |
| Max sentence length: | 512 |
References
Trained with the in-house dataset
Benchmarking
label precision recall f1-score support
No 0.9927 0.9875 0.9901 3031
Yes 0.8430 0.9027 0.8718 226
accuracy - - 0.9816 3257
macro-avg 0.9178 0.9451 0.9309 3257
weighted-avg 0.9823 0.9816 0.9819 3257