Description
This model extracts healthcare-related terms from the documents transferred from the patient’s own sentences.
Note: ‘wip’ suffix indicates that the model development is work-in-progress and will be finalised and the model performance will improved in the upcoming releases.
Predicted Entities
AdmissionDischarge, Age, BodyPart, ClinicalDept, DateTime, Disease, Dosage_Strength, Drug, Duration, Employment, Form, Frequency, Gender, Laterality, Procedure, PsychologicalCondition, RelationshipStatus, Route, Symptom, Test, Vaccine, VitalTest
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", "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_vop_slim_wip", "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,
clinical_embeddings,
ner_model,
ner_converter
])
sample_texts = ["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."]
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_vop_slim_wip", "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,
clinical_embeddings,
ner_model,
ner_converter
))
val data = Seq("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.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+--------------------+-----+----+----------------------+
|chunk |begin|end |ner_label |
+--------------------+-----+----+----------------------+
|20 year old |10 |20 |Age |
|girl |22 |25 |Gender |
|hyperthyroid |47 |58 |Disease |
|1 month ago |60 |70 |DateTime |
|weak |87 |90 |Symptom |
|panic attacks |122 |134 |PsychologicalCondition|
|depression |137 |146 |PsychologicalCondition|
|left |149 |152 |Laterality |
|chest |154 |158 |BodyPart |
|pain |160 |163 |Symptom |
|heart rate |176 |185 |VitalTest |
|weight loss |196 |206 |Symptom |
|4 months |215 |222 |Duration |
|hospital |258 |265 |ClinicalDept |
|discharged |276 |285 |AdmissionDischarge |
|hospital |292 |299 |ClinicalDept |
|blood tests |319 |329 |Test |
|brain |332 |336 |BodyPart |
|mri |338 |340 |Test |
|ultrasound scan |343 |357 |Test |
|endoscopy |360 |368 |Procedure |
|doctors |391 |397 |Employment |
|homeopathy doctor |486 |502 |Employment |
|he |512 |513 |Gender |
|hyperthyroid |546 |557 |Disease |
|TSH |566 |568 |Test |
|T3 |579 |580 |Test |
|T4 |586 |587 |Test |
|b12 deficiency |613 |626 |Disease |
|vitamin D deficiency|632 |651 |Disease |
|weekly |667 |672 |Frequency |
|supplement |674 |683 |Drug |
|vitamin D |688 |696 |Drug |
|1000 mcg |702 |709 |Dosage_Strength |
|b12 |711 |713 |Drug |
|daily |715 |719 |Frequency |
|homeopathy medicine |733 |751 |Drug |
|40 days |757 |763 |Duration |
|after 30 days |783 |795 |DateTime |
|TSH |801 |803 |Test |
|now |812 |814 |DateTime |
|weakness |849 |856 |Symptom |
|depression |862 |871 |PsychologicalCondition|
|last week |912 |920 |DateTime |
|rapid heartrate |960 |974 |Symptom |
|thyroid |1074 |1080|BodyPart |
+--------------------+-----+----+----------------------+
Model Information
| Model Name: | ner_vop_slim_wip |
| Compatibility: | Healthcare NLP 4.3.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 2.4 MB |
Benchmarking
label tp fp fn total precision recall f1
Route 25.0 4.0 15.0 40.0 0.8621 0.625 0.7246
Procedure 161.0 48.0 70.0 231.0 0.7703 0.697 0.7318
Vaccine 22.0 7.0 8.0 30.0 0.7586 0.7333 0.7458
RelationshipStatus 6.0 2.0 2.0 8.0 0.75 0.75 0.75
Disease 884.0 201.0 285.0 1169.0 0.8147 0.7562 0.7844
Frequency 342.0 61.0 113.0 455.0 0.8486 0.7516 0.7972
Duration 720.0 188.0 146.0 866.0 0.793 0.8314 0.8117
Test 478.0 106.0 103.0 581.0 0.8185 0.8227 0.8206
Symptom 1569.0 337.0 340.0 1909.0 0.8232 0.8219 0.8225
DateTime 1558.0 277.0 296.0 1854.0 0.849 0.8403 0.8447
ClinicalDept 157.0 9.0 48.0 205.0 0.9458 0.7659 0.8464
Form 110.0 28.0 11.0 121.0 0.7971 0.9091 0.8494
Dosage_Strength 184.0 25.0 33.0 217.0 0.8804 0.8479 0.8638
Drug 672.0 109.0 103.0 775.0 0.8604 0.8671 0.8638
VitalTest 73.0 7.0 16.0 89.0 0.9125 0.8202 0.8639
Laterality 262.0 43.0 38.0 300.0 0.859 0.8733 0.8661
Age 236.0 42.0 14.0 250.0 0.8489 0.944 0.8939
PsychologicalCond... 144.0 20.0 14.0 158.0 0.878 0.9114 0.8944
BodyPart 1319.0 139.0 160.0 1479.0 0.9047 0.8918 0.8982
Employment 541.0 25.0 77.0 618.0 0.9558 0.8754 0.9139
AdmissionDischarge 13.0 0.0 1.0 14.0 1.0 0.9286 0.963
Gender 548.0 26.0 12.0 560.0 0.9547 0.9786 0.9665