Description
This model extracts staging information and mentions related to tumors, lymph nodes and metastases.
Definitions of Predicted Entities:
Cancer_Dx: Mentions of cancer diagnoses (such as “breast cancer”) or pathological types that are usually used as synonyms for “cancer” (e.g. “carcinoma”). When anatomical references are present, they are included in the Cancer_Dx extraction.Lymph_Node: Mentions of lymph nodes and pathological findings of the lymph nodes.Lymph_Node_Modifier: Words that refer to a lymph node being abnormal (such as “enlargement”).Metastasis: Terms that indicate a metastatic disease. Anatomical references are not included in these extractions.Staging: Mentions of cancer stage such as “stage 2b” or “T2N1M0”. It also includes words such as “in situ”, “early-stage” or “advanced”.Tumor: All nonspecific terms that may be related to tumors, either malignant or benign (for example: “mass”, “tumor”, “lesion”, or “neoplasm”).Tumor_Description: Information related to tumor characteristics, such as size, presence of invasion, grade and hystological type.
Predicted Entities
Cancer_Dx, Lymph_Node, Lymph_Node_Modifier, Metastasis, Staging, Tumor, Tumor_Description
Live Demo Open in Colab 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_oncology_tnm", "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 final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."]]).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_tnm", "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 final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.oncology_tnm").predict("""The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.""")
Results
| chunk | ner_label |
|:-----------------|:------------------|
| metastatic | Metastasis |
| breast carcinoma | Cancer_Dx |
| T2N1M1 stage IV | Staging |
| 4 cm | Tumor_Description |
| tumor | Tumor |
| grade 2 | Tumor_Description |
Model Information
| Model Name: | ner_oncology_tnm |
| Compatibility: | Healthcare NLP 4.2.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 34.2 MB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Lymph_Node 570 77 77 647 0.88 0.88 0.88
Staging 232 22 26 258 0.91 0.90 0.91
Lymph_Node_Modifier 30 5 5 35 0.86 0.86 0.86
Tumor_Description 2651 581 490 3141 0.82 0.84 0.83
Tumor 1116 72 141 1257 0.94 0.89 0.91
Metastasis 358 15 12 370 0.96 0.97 0.96
Cancer_Dx 1302 87 92 1394 0.94 0.93 0.94
macro_avg 6259 859 843 7102 0.90 0.90 0.90
micro_avg 6259 859 843 7102 0.88 0.88 0.88