Detect Clinical Entities (ner_jsl_greedy)

Description

Pretrained named entity recognition deep learning model for clinical terminology. The SparkNLP deep learning model (MedicalNerModel) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN. This model is the official version of jsl_ner_wip_greedy_clinical model.

Predicted Entities

Injury_or_Poisoning, Direction, Test, Admission_Discharge, Death_Entity, Relationship_Status, Duration, Hyperlipidemia, Respiration, Birth_Entity, Age, Family_History_Header, Labour_Delivery, BMI, Temperature, Alcohol, Kidney_Disease, Oncological, Medical_History_Header, Cerebrovascular_Disease, Oxygen_Therapy, O2_Saturation, Psychological_Condition, Heart_Disease, Employment, Obesity, Disease_Syndrome_Disorder, Pregnancy, ImagingFindings, Procedure, Medical_Device, Race_Ethnicity, Section_Header, Drug, Symptom, Treatment, Substance, Route, Blood_Pressure, Diet, External_body_part_or_region, LDL, VS_Finding, Allergen, EKG_Findings, Imaging_Technique, Triglycerides, RelativeTime, Gender, Pulse, Social_History_Header, Substance_Quantity, Diabetes, Modifier, Internal_organ_or_component, Clinical_Dept, Form, Strength, Fetus_NewBorn, RelativeDate, Height, Test_Result, Time, Frequency, Sexually_Active_or_Sexual_Orientation, Weight, Vaccine, Vital_Signs_Header, Communicable_Disease, Dosage, Hypertension, HDL, Overweight, Total_Cholesterol, Smoking, Date.

Live Demo Open in Colab Download

How to use

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

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

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

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

jsl_ner_converter = NerConverter() \
		.setInputCols(["sentence", "token", "jsl_ner"]) \
		.setOutputCol("ner_chunk")

jsl_ner_pipeline = Pipeline().setStages([
				documentAssembler,
				sentenceDetector,
				tokenizer,
				embeddings,
				jsl_ner,
				jsl_ner_converter])


jsl_ner_model = jsl_ner_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))

data = spark.createDataFrame([["""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."""]]).toDF("text")

result = jsl_ner_model.transform(data)
val documentAssembler = new DocumentAssembler()
		.setInputCol("text")
		.setOutputCol("document")

val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
		.setInputCols("document") 
		.setOutputCol("sentence")

val tokenizer = new Tokenizer()
		.setInputCols("sentence")
		.setOutputCol("token")
	
val embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
		.setInputCols(Array("sentence", "token"))
	    	.setOutputCol("embeddings")
  
val jsl_ner = MedicalNerModel.pretrained("ner_jsl_greedy", "en", "clinical/models")
		.setInputCols(Array("sentence", "token", "embeddings"))
		.setOutputCol("jsl_ner")

val jsl_ner_converter = new NerConverter()
		.setInputCols(Array("sentence", "token", "jsl_ner"))
		.setOutputCol("ner_chunk")
 
val jsl_ner_pipeline = new Pipeline().setStages(Array(
					documentAssembler, 
					sentenceDetector, 
					tokenizer, 
					embeddings, 
					jsl_ner, 
					jsl_ner_converter))


val data = Seq("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""").toDS.toDF("text")

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

Results

+----------------------------------------------+----------------------------+
|chunk                                         |ner_label                   |
+----------------------------------------------+----------------------------+
|21-day-old                                    |Age                         |
|Caucasian                                     |Race_Ethnicity              |
|male                                          |Gender                      |
|for 2 days                                    |Duration                    |
|congestion                                    |Symptom                     |
|mom                                           |Gender                      |
|suctioning yellow discharge                   |Symptom                     |
|nares                                         |External_body_part_or_region|
|she                                           |Gender                      |
|mild problems with his breathing while feeding|Symptom                     |
|perioral cyanosis                             |Symptom                     |
|retractions                                   |Symptom                     |
|One day ago                                   |RelativeDate                |
|mom                                           |Gender                      |
|tactile temperature                           |Symptom                     |
|Tylenol                                       |Drug                        |
|Baby                                          |Age                         |
|decreased p.o. intake                         |Symptom                     |
|His                                           |Gender                      |
|20 minutes                                    |Duration                    |
+----------------------------------------------+----------------------------+

Model Information

Model Name: ner_jsl_greedy
Compatibility: Spark NLP for Healthcare 3.1.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en

Data Source

Trained on data gathered and manually annotated by John Snow Labs. https://www.johnsnowlabs.com/data/

Benchmarking

              entity      tp      fp      fn   total  precision  recall      f1
          VS_Finding   229.0    56.0    34.0   263.0     0.8035  0.8707  0.8358
           Direction  4009.0   479.0   403.0  4412.0     0.8933  0.9087  0.9009
Female_Reproducti...     2.0     1.0     3.0     5.0     0.6667     0.4     0.5
         Respiration    80.0     9.0    14.0    94.0     0.8989  0.8511  0.8743
Cerebrovascular_D...    82.0    27.0    18.0   100.0     0.7523    0.82  0.7847
                 not     4.0     0.0     0.0     4.0        1.0     1.0     1.0
Family_History_He...    86.0     4.0     3.0    89.0     0.9556  0.9663  0.9609
       Heart_Disease   469.0    76.0    83.0   552.0     0.8606  0.8496  0.8551
     ImagingFindings    68.0    38.0    75.0   143.0     0.6415  0.4755  0.5462
        RelativeTime   141.0    76.0    66.0   207.0     0.6498  0.6812  0.6651
            Strength   720.0    49.0    58.0   778.0     0.9363  0.9254  0.9308
             Smoking   117.0     8.0     6.0   123.0      0.936  0.9512  0.9435
      Medical_Device  3584.0   730.0   359.0  3943.0     0.8308   0.909  0.8681
        EKG_Findings    41.0    20.0    45.0    86.0     0.6721  0.4767  0.5578
               Pulse   138.0    23.0    24.0   162.0     0.8571  0.8519  0.8545
Psychological_Con...   121.0    14.0    29.0   150.0     0.8963  0.8067  0.8491
          Overweight     5.0     2.0     0.0     5.0     0.7143     1.0  0.8333
       Triglycerides     3.0     0.0     0.0     3.0        1.0     1.0     1.0
             Obesity    49.0     6.0     4.0    53.0     0.8909  0.9245  0.9074
 Admission_Discharge   325.0    30.0     2.0   327.0     0.9155  0.9939  0.9531
                 HDL     2.0     1.0     1.0     3.0     0.6667  0.6667  0.6667
            Diabetes   118.0    13.0     7.0   125.0     0.9008   0.944  0.9219
      Section_Header  3778.0   148.0   138.0  3916.0     0.9623  0.9648  0.9635
                 Age   617.0    52.0    47.0   664.0     0.9223  0.9292  0.9257
       O2_Saturation    34.0    11.0    19.0    53.0     0.7556  0.6415  0.6939
      Kidney_Disease   114.0     5.0    12.0   126.0      0.958  0.9048  0.9306
                Test  2668.0   526.0   498.0  3166.0     0.8353  0.8427   0.839
Communicable_Disease    25.0    12.0     9.0    34.0     0.6757  0.7353  0.7042
        Hypertension   152.0    10.0     6.0   158.0     0.9383   0.962    0.95
External_body_par...  2652.0   387.0   340.0  2992.0     0.8727  0.8864  0.8795
      Oxygen_Therapy    67.0    21.0    23.0    90.0     0.7614  0.7444  0.7528
         Test_Result  1124.0   227.0   258.0  1382.0      0.832  0.8133  0.8225
            Modifier   539.0   185.0   309.0   848.0     0.7445  0.6356  0.6858
                 BMI     7.0     1.0     1.0     8.0      0.875   0.875   0.875
     Labour_Delivery    75.0    19.0    23.0    98.0     0.7979  0.7653  0.7813
          Employment   249.0    51.0    57.0   306.0       0.83  0.8137  0.8218
       Clinical_Dept   948.0    95.0    80.0  1028.0     0.9089  0.9222  0.9155
                Time    36.0     7.0     7.0    43.0     0.8372  0.8372  0.8372
           Procedure  3180.0   460.0   480.0  3660.0     0.8736  0.8689  0.8712
                Diet    50.0    29.0    30.0    80.0     0.6329   0.625  0.6289
         Oncological   478.0    46.0    50.0   528.0     0.9122  0.9053  0.9087
                 LDL     3.0     0.0     2.0     5.0        1.0     0.6    0.75
             Symptom  6801.0  1097.0  1097.0  7898.0     0.8611  0.8611  0.8611
         Temperature   109.0    12.0     7.0   116.0     0.9008  0.9397  0.9198
  Vital_Signs_Header   213.0    27.0    16.0   229.0     0.8875  0.9301  0.9083
 Relationship_Status    42.0     2.0     1.0    43.0     0.9545  0.9767  0.9655
   Total_Cholesterol    10.0     4.0     5.0    15.0     0.7143  0.6667  0.6897
      Blood_Pressure   167.0    22.0    23.0   190.0     0.8836  0.8789  0.8813
 Injury_or_Poisoning   510.0    83.0   111.0   621.0       0.86  0.8213  0.8402
     Drug_Ingredient  1698.0   160.0   158.0  1856.0     0.9139  0.9149  0.9144
           Treatment   156.0    40.0    54.0   210.0     0.7959  0.7429  0.7685
Assertion_SocialD...     4.0     0.0     6.0    10.0        1.0     0.4  0.5714
           Pregnancy   100.0    45.0    41.0   141.0     0.6897  0.7092  0.6993
             Vaccine    13.0     3.0     6.0    19.0     0.8125  0.6842  0.7429
Disease_Syndrome_...  2861.0   452.0   376.0  3237.0     0.8636  0.8838  0.8736
              Height    25.0     8.0     9.0    34.0     0.7576  0.7353  0.7463
           Frequency   650.0   157.0   148.0   798.0     0.8055  0.8145    0.81
               Route   872.0    83.0    85.0   957.0     0.9131  0.9112  0.9121
        Death_Entity    49.0     7.0     6.0    55.0      0.875  0.8909  0.8829
            Duration   367.0   132.0    95.0   462.0     0.7355  0.7944  0.7638
Internal_organ_or...  6532.0  1016.0   987.0  7519.0     0.8654  0.8687  0.8671
             Alcohol    79.0    20.0    12.0    91.0      0.798  0.8681  0.8316
                Date   515.0    19.0    19.0   534.0     0.9644  0.9644  0.9644
      Hyperlipidemia    47.0     2.0     1.0    48.0     0.9592  0.9792  0.9691
Social_History_He...    89.0     9.0     4.0    93.0     0.9082   0.957  0.9319
      Race_Ethnicity   113.0     0.0     3.0   116.0        1.0  0.9741  0.9869
   Imaging_Technique    47.0    31.0    30.0    77.0     0.6026  0.6104  0.6065
      Drug_BrandName   963.0    72.0    79.0  1042.0     0.9304  0.9242  0.9273
        RelativeDate   553.0   128.0   121.0   674.0      0.812  0.8205  0.8162
              Gender  6043.0    59.0    87.0  6130.0     0.9903  0.9858  0.9881
                Form   227.0    35.0    47.0   274.0     0.8664  0.8285   0.847
              Dosage   279.0    42.0    62.0   341.0     0.8692  0.8182  0.8429
Medical_History_H...   117.0     4.0    11.0   128.0     0.9669  0.9141  0.9398
           Substance    59.0    16.0    16.0    75.0     0.7867  0.7867  0.7867
              Weight    85.0    19.0    21.0   106.0     0.8173  0.8019  0.8095
               macro     -       -       -       -         -       -     0.7286
               micro     -       -       -       -         -       -     0.8715