Healthcare NLP v3.2.3 Release Notes

3.2.3

We are glad to announce that Spark NLP Healthcare 3.2.3 has been released!.

Highlights

New BERT-Based Deidentification NER Model
New Sentence Entity Resolver Models For German Language
New Spell Checker Model For Drugs
Allow To Use Disambiguator Pretrained Model
Allow To Use Seeds in StructuredDeidentification
Added Compatibility with Tensorflow 1.15 For Graph Generation.
New Setup Videos

New BERT-Based Deidentification NER Model

We have a new bert_token_classifier_ner_deid model that is BERT-based version of ner_deid_subentity_augmented and annotates text to find protected health information that may need to be de-identified. It can detect 23 different entities (MEDICALRECORD, ORGANIZATION, DOCTOR, USERNAME, PROFESSION, HEALTHPLAN, URL, CITY, DATE, LOCATION-OTHER, STATE, PATIENT, DEVICE, COUNTRY, ZIP, PHONE, HOSPITAL, EMAIL, IDNUM, SREET, BIOID, FAX, AGE).

Example:

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

tokenizer = Tokenizer()\
  .setInputCols("document")\
  .setOutputCol("token")

tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_ner_deid", "en")\
  .setInputCols("token", "document")\
  .setOutputCol("ner")\
  .setCaseSensitive(True)

ner_converter = NerConverter()\
  .setInputCols(["document","token","ner"])\
  .setOutputCol("ner_chunk")

pipeline =  Pipeline(stages=[documentAssembler, tokenizer, tokenClassifier, ner_converter])
p_model = pipeline.fit(spark.createDataFrame(pd.DataFrame({'text': ['']})))

text = """A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."""
result = p_model.transform(spark.createDataFrame(pd.DataFrame({'text': [text]})))

Results:

+-----------------------------+-------------+
|chunk                        |ner_label    |
+-----------------------------+-------------+
|2093-01-13                   |DATE         |
|David Hale                   |DOCTOR       |
|Hendrickson, Ora             |PATIENT      |
|7194334                      |MEDICALRECORD|
|Oliveira                     |PATIENT      |
|Cocke County Baptist Hospital|HOSPITAL     |
|0295 Keats Street            |STREET       |
|302) 786-5227                |PHONE        |
|Brothers Coal-Mine           |ORGANIZATION |
+-----------------------------+-------------+

New Sentence Entity Resolver Models For German Language

We are releasing two new Sentence Entity Resolver Models for German language that use sent_bert_base_cased (de) embeddings.

sbertresolve_icd10gm : This model maps extracted medical entities to ICD10-GM codes for the German language.

Example:

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

sbert_embedder = BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "de")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("sbert_embeddings")

icd10gm_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_icd10gm", "de", "clinical/models")\
    .setInputCols(["sbert_embeddings"])\
    .setOutputCol("icd10gm_code")

icd10gm_pipelineModel = PipelineModel( stages = [documentAssembler, sbert_embedder, icd10gm_resolver])

icd_lp = LightPipeline(icd10gm_pipelineModel)
icd_lp.fullAnnotate("Dyspnoe")

Results :

chunk	code	resolutions	all_codes	all_distances
Dyspnoe	C671	Dyspnoe, Schlafapnoe, Dysphonie, Frühsyphilis, Hyperzementose, Hypertrichose, …	[R06.0, G47.3, R49.0, A51, K03.4, L68, …]	[0.0000, 2.5602, 3.0529, 3.3310, 3.4645, 3.7148, …]

sbertresolve_snomed : This model maps extracted medical entities to SNOMED codes for the German language.

Example:

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

sbert_embedder = BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "de")\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("sbert_embeddings")

snomed_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_snomed", "de", "clinical/models")\
    .setInputCols(["sbert_embeddings"])\
    .setOutputCol("snomed_code")

snomed_pipelineModel = PipelineModel( stages = [ documentAssembler, sbert_embedder, snomed_resolver])

snomed_lp = LightPipeline(snomed_pipelineModel)
snomed_lp.fullAnnotate("Bronchialkarzinom ")

Results :

chunk	code	resolutions	all_codes	all_distances
Bronchialkarzinom	22628	Bronchialkarzinom, Bronchuskarzinom, Rektumkarzinom, Klavikulakarzinom, Lippenkarzinom, Urothelkarzinom, …	[22628, 111139, 18116, 107569, 18830, 22909, …]	[0.0000, 0.0073, 0.0090, 0.0098, 0.0098, 0.0102, …]

New Spell Checker Model For Drugs

We are releasing new spellcheck_drug_norvig model that detects and corrects spelling errors of drugs in a text based on the Norvig’s approach.

Example :

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

tokenizer = Tokenizer()
    .setInputCols("document")\
    .setOutputCol("token")

spell = NorvigSweetingModel.pretrained("spellcheck_drug_norvig", "en", "clinical/models")\
    .setInputCols("token")
    .setOutputCol("spell")\

pipeline = Pipeline( stages = [documentAssembler,
tokenizer, spell])

model = pipeline.fit(spark.createDataFrame([['']]).toDF('text'))
lp = LightPipeline(model)

lp.annotate("You have to take Neutrcare and colfosrinum and a bit of Fluorometholne & Ribotril")

Results :

Original text  : You have to take Neutrcare and colfosrinum and a bit of fluorometholne & Ribotril
Corrected text : You have to take Neutracare and colforsinum and a bit of fluorometholone & Rivotril

Allow to use Disambiguator pretrained model.

Now we can use the NerDisambiguatorModel as a pretrained model to disambiguate person entities.

 text = "The show also had a contestant named Brad Pitt" \
        + "who later defeated Christina Aguilera on the way to become Female Vocalist Champion in the 1989 edition of Star Search in the United States. "
 data = SparkContextForTest.spark.createDataFrame([
     [text]]) \
     .toDF("text").cache()
 da = DocumentAssembler().setInputCol("text").setOutputCol("document")

 sd = SentenceDetector().setInputCols("document").setOutputCol("sentence")

 tk = Tokenizer().setInputCols("sentence").setOutputCol("token")

 emb = WordEmbeddingsModel.pretrained().setOutputCol("embs")

 semb = SentenceEmbeddings().setInputCols("sentence", "embs").setOutputCol("sentence_embeddings")

 ner = NerDLModel.pretrained().setInputCols("sentence", "token", "embs").setOutputCol("ner")

 nc = NerConverter().setInputCols("sentence", "token", "ner").setOutputCol("ner_chunk").setWhiteList(["PER"])

 NerDisambiguatorModel.pretrained().setInputCols("ner_chunk", "sentence_embeddings").setOutputCol("disambiguation")

 pl = Pipeline().setStages([da, sd, tk, emb, semb, ner, nc, disambiguator])

 data = pl.fit(data).transform(data)
 data.select("disambiguation").show(10, False)

+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|disambiguation                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[[disambiguation, 65, 82, https://en.wikipedia.org/?curid=144171, https://en.wikipedia.org/?curid=6636454, [chunk -> Christina Aguilera, titles -> christina aguilera ::::: christina aguilar, links -> https://en.wikipedia.org/?curid=144171 ::::: https://en.wikipedia.org/?curid=6636454, beginInText -> 65, scores -> 0.9764155197864447, 0.9727793647472524, categories -> Musicians, Singers, Actors, Businesspeople, Musicians, Singers, ids -> 144171, 6636454, endInText -> 82], []]]|
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

----------------------

Allow to use seeds in StructuredDeidentification

Now, we can use a seed for a specific column. The seed is used to randomly select the entities used during obfuscation mode. By providing the same seed, you can replicate the same mapping multiple times.

df = spark.createDataFrame([
            ["12", "12", "Juan García"],
            ["24", "56", "Will Smith"],
            ["56", "32", "Pedro Ximénez"]
        ]).toDF("ID1", "ID2", "NAME")

obfuscator = StructuredDeidentification(spark=spark, columns={"ID1": "ID", "ID2": "ID", "NAME": "PATIENT"},
                                                columnsSeed={"ID1": 23, "ID2": 23},
                                                obfuscateRefSource="faker")
result = obfuscator.obfuscateColumns(df)
result.show(truncate=False)      

+----------+----------+----------------+
|ID1       |ID2       |NAME            |
+----------+----------+----------------+
|[D3379888]|[D3379888]|[Raina Cleaves] |
|[R8448971]|[M8851891]|[Jennell Barre] |
|[M8851891]|[L5448098]|[Norene Salines]|
+----------+----------+----------------+

Here, you can see that as we have provided the same seed `23` for columns `ID1`, and `ID2`, the number `12` which is appears twice in the first row is mapped to the same randomly generated id `D3379888` each time.

Added compatibility with Tensorflow 1.15 for graph generation

Some users reported problems while using graphs generated by Tensorflow 2.x. We provide compatibility with Tensorflow 1.15 in the tf_graph_1x module, that can be used like this,

from sparknlp_jsl.training import tf_graph_1x

In next releases, we will provide full support for graph generation using Tensorflow 2.x.

New Setup Videos

Now we have videos showing how to setup Spark NLP, Spark NLP for Healthcare and Spark OCR on UBUNTU.

To see more, please check: Spark NLP Healthcare Workshop Repo

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