Healthcare NLP v3.1.3 Release Notes

 

3.1.3

We are glad to announce that Spark NLP for Healthcare 3.1.3 has been released!. This release comes with new features, new models, bug fixes, and examples.

Highlights

  • New Relation Extraction model and a Pretrained pipeline for extracting and linking ADEs
  • New Entity Resolver model for SNOMED codes
  • ChunkConverter Annotator
  • BugFix: getAnchorDateMonth method in DateNormalizer.
  • BugFix: character map in MedicalNerModel fine-tuning.
New Relation Extraction model and a Pretrained pipeline for extracting and linking ADEs

We are releasing a new Relation Extraction Model for ADEs. This model is trained using Bert Word embeddings (biobert_pubmed_base_cased), and is capable of linking ADEs and Drugs.

Example:

re_model = RelationExtractionModel()\
        .pretrained("re_ade_biobert", "en", 'clinical/models')\
        .setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])\
        .setOutputCol("relations")\
        .setMaxSyntacticDistance(3)\ #default: 0
        .setPredictionThreshold(0.5)\ #default: 0.5
        .setRelationPairs(["ade-drug", "drug-ade"]) # Possible relation pairs. Default: All Relations.

nlp_pipeline = Pipeline(stages=[documenter, sentencer, tokenizer, words_embedder, pos_tagger, ner_tagger, ner_chunker, dependency_parser, re_model])

light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))

text ="""Been taking Lipitor for 15 years , have experienced sever fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""

annotations = light_pipeline.fullAnnotate(text)

We also have a new pipeline comprising of all models related to ADE(Adversal Drug Event) as part of this release. This pipeline includes classification, NER, assertion and relation extraction models. Users can now use this pipeline to get classification result, ADE and Drug entities, assertion status for ADE entities, and relations between ADE and Drug entities.

Example:

    pretrained_ade_pipeline = PretrainedPipeline('explain_clinical_doc_ade', 'en', 'clinical/models')

    result = pretrained_ade_pipeline.fullAnnotate("""Been taking Lipitor for 15 years , have experienced sever fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")[0]

Results:

Class: True

NER_Assertion:
|    | chunk                   | entitiy    | assertion   |
|----|-------------------------|------------|-------------|
| 0  | Lipitor                 | DRUG       | -           |
| 1  | sever fatigue           | ADE        | Conditional |
| 2  | voltaren                | DRUG       | -           |
| 3  | cramps                  | ADE        | Conditional |

Relations:
|    | chunk1                        | entitiy1   | chunk2      | entity2 | relation |
|----|-------------------------------|------------|-------------|---------|----------|
| 0  | sever fatigue                 | ADE        | Lipitor     | DRUG    |        1 |
| 1  | cramps                        | ADE        | Lipitor     | DRUG    |        0 |
| 2  | sever fatigue                 | ADE        | voltaren    | DRUG    |        0 |
| 3  | cramps                        | ADE        | voltaren    | DRUG    |        1 |
New Entity Resolver model for SNOMED codes

We are releasing a new SentenceEntityResolver model for SNOMED codes. This model also includes AUX SNOMED concepts and can find codes for Morph Abnormality, Procedure, Substance, Physical Object, and Body Structure entities. In the metadata, the all_k_aux_labels can be divided to get further information: ground truth, concept, and aux . In the example shared below the ground truth is Atherosclerosis, concept is Observation, and aux is Morph Abnormality.

Example:

snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models") \
     .setInputCols(["sbert_embeddings"]) \
     .setOutputCol("snomed_code")\
     .setDistanceFunction("EUCLIDEAN")

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

snomed_lp = LightPipeline(snomed_pipelineModel)
result = snomed_lp.fullAnnotate("atherosclerosis")

Results:

|    | chunks          | code     | resolutions                                                                                                                                                                                                                                                                                                                                                                                                                    | all_codes                                                                                                                                                                                          | all_k_aux_labels                                      | all_distances                                                                                                                                   |
|---:|:----------------|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------|
|  0 | atherosclerosis | 38716007 | [atherosclerosis, atherosclerosis, atherosclerosis, atherosclerosis, atherosclerosis, atherosclerosis, atherosclerosis artery, coronary atherosclerosis, coronary atherosclerosis, coronary atherosclerosis, coronary atherosclerosis, coronary atherosclerosis, arteriosclerosis, carotid atherosclerosis, cardiovascular arteriosclerosis, aortic atherosclerosis, aortic atherosclerosis, atherosclerotic ischemic disease] | [38716007, 155382007, 155414001, 195251000, 266318005, 194848007, 441574008, 443502000, 41702007, 266231003, 155316000, 194841001, 28960008, 300920004, 39468009, 155415000, 195252007, 129573006] | 'Atherosclerosis', 'Observation', 'Morph Abnormality' | [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0280, 0.0451, 0.0451, 0.0451, 0.0451, 0.0451, 0.0462, 0.0477, 0.0466, 0.0490, 0.0490, 0.0485 |
ChunkConverter Annotator

Allows to use RegexMather chunks as NER chunks and feed the output to the downstream annotators like RE or Deidentification.

        document_assembler = DocumentAssembler().setInputCol('text').setOutputCol('document')

        sentence_detector = SentenceDetector().setInputCols(["document"]).setOutputCol("sentence")

        regex_matcher = RegexMatcher()\
            .setInputCols("sentence")\
            .setOutputCol("regex")\
            .setExternalRules(path="../src/test/resources/regex-matcher/rules.txt",delimiter=",")

        chunkConverter = ChunkConverter().setInputCols("regex").setOutputCol("chunk")

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