Description
This model extracts demographic information from oncology texts, including age, gender, and smoking status. It is the version of ner_oncology_demographics model augmented with langtest library
Definitions of Predicted Entities:
Age: All mention of ages, past or present, related to the patient or with anybody else.Gender: Gender-specific nouns and pronouns (including words such as “him” or “she”, and family members such as “father”).Race_Ethnicity: The race and ethnicity categories include racial and national origin or sociocultural groups.Smoking_Status: All mentions of smoking related to the patient or to someone else.
Predicted Entities
Age, Gender, Race_Ethnicity, Smoking_Status
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_oncology_demographics_langtest", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
ner_converter])
data = spark.createDataFrame([["The patient is a 40 year old man with history of heavy smoking."]]).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_oncology_demographics_langtest", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.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("The patient is a 40 year old man with history of heavy smoking.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-----------+--------------+
|chunk |ner_label |
+-----------+--------------+
|40 year old|Age |
|man |Gender |
|smoking |Smoking_Status|
+-----------+--------------+
Model Information
| Model Name: | ner_oncology_demographics_langtest |
| Compatibility: | Healthcare NLP 5.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.8 MB |
References
In-house annotated oncology case reports.
Benchmarking
label precision recall f1-score support
B-Gender 0.99 1.00 0.99 1235
B-Age 0.97 0.96 0.97 224
I-Age 0.98 0.99 0.99 799
B-Smoking_Status 0.93 0.89 0.91 57
I-Gender 0.00 0.00 0.00 1
B-Race_Ethnicity 0.87 1.00 0.93 45
I-Race_Ethnicity 0.71 0.83 0.77 6
I-Smoking_Status 0.67 0.91 0.77 11
micro-avg 0.98 0.99 0.98 2378
macro-avg 0.76 0.82 0.79 2378
weighted-avg 0.98 0.99 0.98 2378