Description
This LLM model is trained to extract and link entities in a document. Users needs to define an input schema as explained in the example section. Drug is defined as a list which tells the model that there could be multiple drugs in the document and it has to extract all of them. Each drug has properties like name and reaction. Since “name” is only one, it is a string, but there could be multiple reactions, hence it is a list. Similarly, users can define any schema for any type of entity.
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
medical_llm = MedicalLLM.pretrained("jsl_medm_q4_v3", "en", "clinical/models")\
.setInputCols("document")\
.setOutputCol("completions")\
.setBatchSize(1)\
.setNPredict(100)\
.setUseChatTemplate(True)\
.setTemperature(0)
pipeline = Pipeline(
stages = [
document_assembler,
medical_llm
])
prompt = """
A 23-year-old pregnant woman at 22 weeks gestation presents with burning upon urination. She states it started 1 day ago and has been worsening despite drinking more water and taking cranberry extract. She otherwise feels well and is followed by a doctor for her pregnancy. Her temperature is 97.7°F (36.5°C), blood pressure is 122/77 mmHg, pulse is 80/min, respirations are 19/min, and oxygen saturation is 98% on room air. Physical exam is notable for an absence of costovertebral angle tenderness and a gravid uterus.
Which of the following is the best treatment for this patient?
A: Ampicillin
B: Ceftriaxone
C: Ciprofloxacin
D: Doxycycline
E: Nitrofurantoin
"""
data = spark.createDataFrame([[prompt]]).toDF("text")
results = pipeline.fit(data).transform(data)
results.select("completions").show(truncate=False)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val medical_llm = MedicalLLM.pretrained("jsl_medm_q4_v3", "en", "clinical/models")
.setInputCols("document")
.setOutputCol("completions")
.setBatchSize(1)
.setNPredict(100)
.setUseChatTemplate(True)
.setTemperature(0)
val pipeline = new Pipeline().setStages(Array(
document_assembler,
medical_llm
))
val prompt = """
A 23-year-old pregnant woman at 22 weeks gestation presents with burning upon urination. She states it started 1 day ago and has been worsening despite drinking more water and taking cranberry extract. She otherwise feels well and is followed by a doctor for her pregnancy. Her temperature is 97.7°F (36.5°C), blood pressure is 122/77 mmHg, pulse is 80/min, respirations are 19/min, and oxygen saturation is 98% on room air. Physical exam is notable for an absence of costovertebral angle tenderness and a gravid uterus.
Which of the following is the best treatment for this patient?
A: Ampicillin
B: Ceftriaxone
C: Ciprofloxacin
D: Doxycycline
E: Nitrofurantoin
"""
val data = Seq(prompt).toDF("text")
val results = pipeline.fit(data).transform(data)
results.select("completions").show(truncate=False)
Results
The best treatment for a pregnant woman at 22 weeks gestation presenting with symptoms of a urinary tract infection (UTI) is:
E: Nitrofurantoin
Here's the rationale:
- The patient's symptoms of burning upon urination, worsening over a day, and absence of costovertebral angle tenderness suggest a urinary tract infection (UTI).
- The patient is pregnant, which increases the risk of UTIs and their complications, such as pyelonephritis
Model Information
| Model Name: | jsl_medm_q4_v3 |
| Compatibility: | Healthcare NLP 5.5.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 8.8 GB |
Benchmarking
We have generated a total of 400 questions, 100 from each category. These questions were labeled and reviewed by 3 physician annotators. % indicates the preference rate.
Please see the more benchmark information here.
## Overall
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|----------|--------------|----------------------|---------------|
| JSL-MedM | 0.29 | 0.25 | 0.50 |
| ChatGPT | 0.21 | 0.30 | 0.26 |
| Neutral | 0.43 | 0.38 | 0.17 |
| None | 0.07 | 0.07 | 0.08 |
| total | 1.00 | 1.00 | 1.00 |
## Summary
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|----------|--------------|----------------------|---------------|
| JSL-MedM | 0.42 | 0.42 | 0.50 |
| GPT4o | 0.33 | 0.33 | 0.28 |
| Neutral | 0.17 | 0.17 | 0.12 |
| None | 0.08 | 0.08 | 0.10 |
| Total | 1.00 | 1.00 | 1.00 |
## QA
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|----------|--------------|----------------------|---------------|
| JSL-MedM | 0.40 | 0.36 | 0.60 |
| GPT4o | 0.15 | 0.19 | 0.19 |
| Neutral | 0.38 | 0.38 | 0.11 |
| None | 0.08 | 0.08 | 0.09 |
| Total | 1.00 | 1.00 | 1.00 |
## BioMedical
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|----------|--------------|----------------------|---------------|
| JSL-MedM | 0.22 | 0.14 | 0.55 |
| GPT4o | 0.21 | 0.36 | 0.23 |
| Neutral | 0.49 | 0.44 | 0.14 |
| None | 0.07 | 0.06 | 0.07 |
| Total | 1.00 | 1.00 | 1.00 |
## OpenEnded
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|----------|--------------|----------------------|---------------|
| JSL-MedM | 0.21 | 0.19 | 0.38 |
| GPT4o | 0.18 | 0.30 | 0.31 |
| Neutral | 0.55 | 0.46 | 0.26 |
| None | 0.05 | 0.05 | 0.06 |
| Total | 1.00 | 1.00 | 1.00 |
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