Extract Clinical Problem Entities (low granularity) from Voice of the Patient Documents (embeddings_clinical)

Description

This model extracts clinical problems from the documents transferred from the patient’s own sentences. The taxonomy is reduced (one label for all clinical problems).

Predicted Entities

Problem, HealthStatus, Modifier

Live Demo Open in Colab Copy S3 URI

How to use

document_assembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer() \
    .setInputCols(["sentence"]) \
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("embeddings")                

ner = MedicalNerModel.pretrained("ner_vop_problem_reduced", "en", "clinical/models") \
    .setInputCols(["sentence", "token", "embeddings"]) \
    .setOutputCol("ner")

ner_converter = NerConverterInternal() \
    .setInputCols(["sentence", "token", "ner"]) \
    .setOutputCol("ner_chunk")

pipeline = Pipeline(stages=[document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter])

data = spark.createDataFrame([["I've been experiencing joint pain and fatigue lately, so I went to the rheumatology department. After some tests, they diagnosed me with rheumatoid arthritis and started me on a treatment plan to manage the symptoms."]]).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_healthcare","en","clinical/models")
    .setInputCols("document")
    .setOutputCol("sentence")
    
val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")
    
val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")                
    
val ner = MedicalNerModel.pretrained("ner_vop_problem_reduced", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("ner")
    
val ner_converter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")

        
val pipeline = new Pipeline().setStages(Array(document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter))    

val data = Seq("I've been experiencing joint pain and fatigue lately, so I went to the rheumatology department. After some tests, they diagnosed me with rheumatoid arthritis and started me on a treatment plan to manage the symptoms.").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

Results

| chunk                | ner_label   |
|:---------------------|:------------|
| pain                 | Problem     |
| fatigue              | Problem     |
| rheumatoid arthritis | Problem     |

Model Information

Model Name: ner_vop_problem_reduced
Compatibility: Healthcare NLP 4.4.3+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Size: 3.8 MB
Dependencies: embeddings_clinical

References

In-house annotated health-related text in colloquial language.

Sample text from the training dataset

Hello,I’m 20 year old girl. I’m diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I’m taking weekly supplement of vitamin D and 1000 mcg b12 daily. I’m taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I’m facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.

Benchmarking

       label   tp  fp   fn  total  precision  recall   f1
     Problem 5871 617 1339   7210       0.90    0.81 0.86
HealthStatus   86  30   21    107       0.74    0.80 0.77
    Modifier  818 196  321   1139       0.81    0.72 0.76
   macro_avg 6775 843 1681   8456       0.82    0.78 0.80
   micro_avg 6775 843 1681   8456       0.89    0.80 0.85