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
SubstanceQuantity, Measurements, Treatment, Modifier, RaceEthnicity, Allergen, TestResult, InjuryOrPoisoning, Frequency, MedicalDevice, Procedure, Duration, DateTime, HealthStatus, Route, Vaccine, Disease, Symptom, RelationshipStatus, Dosage, Substance, VitalTest, AdmissionDischarge, Test, Laterality, ClinicalDept, PsychologicalCondition, Age, BodyPart, Drug, Employment, Form
Live Demo Open in Colab Download 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_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,
word_embeddings,
ner,
ner_converter])
data = spark.createDataFrame([["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."]]).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_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,
word_embeddings,
ner,
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 | ner_label |
|:----------------------------|:-----------------------|
| 20 year old | Age |
| girl | Gender |
| hyperthyroid | Disease |
| 1 month ago | DateTime |
| weak | Symptom |
| light headed,poor digestion | Symptom |
| panic attacks | PsychologicalCondition |
| depression | PsychologicalCondition |
| left | Laterality |
| chest | BodyPart |
| pain | Symptom |
| increased | TestResult |
| heart rate | VitalTest |
| rapidly | Modifier |
| weight loss | Symptom |
| 4 months | Duration |
| hospital | ClinicalDept |
| discharged | AdmissionDischarge |
| hospital | ClinicalDept |
| blood tests | Test |
| brain | BodyPart |
| mri | Test |
| ultrasound scan | Test |
| endoscopy | Procedure |
| doctors | Employment |
| homeopathy doctor | Employment |
| he | Gender |
| hyperthyroid | Disease |
| TSH | Test |
| 0.15 | TestResult |
| T3 | Test |
| T4 | Test |
| normal | TestResult |
| b12 deficiency | Disease |
| vitamin D deficiency | Disease |
| weekly | Frequency |
| supplement | Drug |
| vitamin D | Drug |
| 1000 mcg | Dosage |
| b12 | Drug |
| daily | Frequency |
| homeopathy medicine | Drug |
| 40 days | Duration |
| 2nd test | Test |
| after 30 days | DateTime |
| TSH | Test |
| 0.5 | TestResult |
| now | DateTime |
| weakness | Symptom |
| depression | PsychologicalCondition |
| last week | DateTime |
| rapid | TestResult |
| heartrate | VitalTest |
| allopathy medicine | Treatment |
| homeopathy | Treatment |
| thyroid | BodyPart |
Model Information
| Model Name: | ner_vop_wip |
| Compatibility: | Healthcare NLP 4.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 3.9 MB |
| Dependencies: | embeddings_clinical |
References
In-house annotated health-related text in colloquial language.
Benchmarking
label tp fp fn total precision recall f1
SubstanceQuantity 9 6 8 17 0.60 0.53 0.56
Measurements 30 15 31 61 0.67 0.49 0.57
Treatment 67 32 26 93 0.68 0.72 0.70
Modifier 638 253 184 822 0.72 0.78 0.74
RaceEthnicity 5 0 3 8 1.00 0.63 0.77
Allergen 7 0 4 11 1.00 0.64 0.78
TestResult 371 110 95 466 0.77 0.80 0.78
InjuryOrPoisoning 113 27 30 143 0.81 0.79 0.80
Frequency 424 81 112 536 0.84 0.79 0.81
MedicalDevice 186 42 39 225 0.82 0.83 0.82
Procedure 288 67 63 351 0.81 0.82 0.82
Duration 786 165 181 967 0.83 0.81 0.82
DateTime 1738 409 288 2026 0.81 0.86 0.83
HealthStatus 76 24 5 81 0.76 0.94 0.84
Route 38 2 13 51 0.95 0.75 0.84
Vaccine 22 3 5 27 0.88 0.81 0.85
Disease 1168 228 149 1317 0.84 0.89 0.86
Symptom 2877 646 318 3195 0.82 0.90 0.86
RelationshipStatus 18 0 5 23 1.00 0.78 0.88
Dosage 268 36 39 307 0.88 0.87 0.88
Substance 167 28 19 186 0.86 0.90 0.88
VitalTest 122 13 16 138 0.90 0.88 0.89
AdmissionDischarge 25 2 4 29 0.93 0.86 0.89
Test 699 84 93 792 0.89 0.88 0.89
Laterality 456 41 59 515 0.92 0.89 0.90
ClinicalDept 169 14 24 193 0.92 0.88 0.90
PsychologicalCondition 264 30 14 278 0.90 0.95 0.92
Age 250 24 17 267 0.91 0.94 0.92
BodyPart 2344 189 135 2479 0.93 0.95 0.94
Drug 962 53 46 1008 0.95 0.95 0.95
Employment 863 50 47 910 0.95 0.95 0.95
Form 201 15 7 208 0.93 0.97 0.95
Gender 1196 28 12 1208 0.98 0.99 0.98
macro_avg 16847 2717 2091 18938 0.86 0.83 0.84
micro_avg 16847 2717 2091 18938 0.86 0.89 0.88