Description
This model extracts more than 40 oncology-related entities, including therapies, tests and staging.
Definitions of Predicted Entities:
Adenopathy: Mentions of pathological findings of the lymph nodes.
Age: All mention of ages, past or present, related to the patient or with anybody else.
Biomarker: Biological molecules that indicate the presence or absence of cancer, or the type of cancer. Oncogenes are excluded from this category.
Biomarker_Result: Terms or values that are identified as the result of a biomarkers.
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.
Cancer_Score: Clinical or imaging scores that are specific for cancer settings (e.g. “BI-RADS” or “Allred score”).
Cancer_Surgery: Terms that indicate surgery as a form of cancer treatment.
Chemotherapy: Mentions of chemotherapy drugs, or unspecific words such as “chemotherapy”.
Cycle_Coun: The total number of cycles being administered of an oncological therapy (e.g. “5 cycles”).
Cycle_Day: References to the day of the cycle of oncological therapy (e.g. “day 5”).
Cycle_Number: The number of the cycle of an oncological therapy that is being applied (e.g. “third cycle”).
Date: Mentions of exact dates, in any format, including day number, month and/or year.
Death_Entity: Words that indicate the death of the patient or someone else (including family members), such as “died” or “passed away”.
Direction: Directional and laterality terms, such as “left”, “right”, “bilateral”, “upper” and “lower”.
Dosage: The quantity prescribed by the physician for an active ingredient.
Duration: Words indicating the duration of a treatment (e.g. “for 2 weeks”).
Frequency: Words indicating the frequency of treatment administration (e.g. “daily” or “bid”).
Gender: Gender-specific nouns and pronouns (including words such as “him” or “she”, and family members such as “father”).
Grade: All pathological grading of tumors (e.g. “grade 1”) or degrees of cellular differentiation (e.g. “well-differentiated”)
Histological_Type: Histological variants or cancer subtypes, such as “papillary”, “clear cell” or “medullary”.
Hormonal_Therapy: Mentions of hormonal drugs used to treat cancer, or unspecific words such as “hormonal therapy”.
Imaging_Test: Imaging tests mentioned in texts, such as “chest CT scan”.
Immunotherapy: Mentions of immunotherapy drugs, or unspecific words such as “immunotherapy”.
Invasion: Mentions that refer to tumor invasion, such as “invasion” or “involvement”. Metastases or lymph node involvement are excluded from this category.
Line_Of_Therapy: Explicit references to the line of therapy of an oncological therapy (e.g. “first-line treatment”).
Metastasis: Terms that indicate a metastatic disease. Anatomical references are not included in these extractions.
Oncogene: Mentions of genes that are implicated in the etiology of cancer.
Pathology_Result: The findings of a biopsy from the pathology report that is not covered by another entity (e.g. “malignant ductal cells”).
Pathology_Test: Mentions of biopsies or tests that use tissue samples.
Performance_Status: Mentions of performance status scores, such as ECOG and Karnofsky. The name of the score is extracted together with the result (e.g. “ECOG performance status of 4”).
Race_Ethnicity: The race and ethnicity categories include racial and national origin or sociocultural groups.
Radiotherapy: Terms that indicate the use of Radiotherapy.
Response_To_Treatment: Terms related to clinical progress of the patient related to cancer treatment, including “recurrence”, “bad response” or “improvement”.
Relative_Date: Temporal references that are relative to the date of the text or to any other specific date (e.g. “yesterday” or “three years later”).
Route: Words indicating the type of administration route (such as “PO” or “transdermal”).
Site_Bone: Anatomical terms that refer to the human skeleton.
Site_Brain: Anatomical terms that refer to the central nervous system (including the brain stem and the cerebellum).
Site_Breast: Anatomical terms that refer to the breasts.
Site_Liver: Anatomical terms that refer to the liver.
Site_Lung: Anatomical terms that refer to the lungs.
Site_Lymph_Node: Anatomical terms that refer to lymph nodes, excluding adenopathies.
Site_Other_Body_Part: Relevant anatomical terms that are not included in the rest of the anatomical entities.
Smoking_Status: All mentions of smoking related to the patient or to someone else.
Staging: Mentions of cancer stage such as “stage 2b” or “T2N1M0”. It also includes words such as “in situ”, “early-stage” or “advanced”.
Targeted_Therapy: Mentions of targeted therapy drugs, or unspecific words such as “targeted therapy”.
Tumor_Finding: All nonspecific terms that may be related to tumors, either malignant or benign (for example: “mass”, “tumor”, “lesion”, or “neoplasm”).
Tumor_Size: Size of the tumor, including numerical value and unit of measurement (e.g. “3 cm”).
Unspecific_Therapy: Terms that indicate a known cancer therapy but that is not specific to any other therapy entity (e.g. “chemoradiotherapy” or “adjuvant therapy”).
Predicted Entities
Histological_Type, Direction, Staging, Cancer_Score, Imaging_Test, Cycle_Number, Tumor_Finding, Site_Lymph_Node, Invasion, Response_To_Treatment, Smoking_Status, Tumor_Size, Cycle_Count, Adenopathy, Age, Biomarker_Result, Unspecific_Therapy, Site_Breast, Chemotherapy, Targeted_Therapy, Radiotherapy, Performance_Status, Pathology_Test, Site_Other_Body_Part, Cancer_Surgery, Line_Of_Therapy, Pathology_Result, Hormonal_Therapy, Site_Bone, Biomarker, Immunotherapy, Cycle_Day, Frequency, Route, Duration, Death_Entity, Metastasis, Site_Liver, Cancer_Dx, Grade, Date, Site_Lung, Site_Brain, Relative_Date, Race_Ethnicity, Gender, Oncogene, Dosage, Radiation_Dose
Live Demo Open in Colab Download 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_medium", "en", "clinical/models")\
.setInputCols(["sentence", "token"]) \
.setOutputCol("embeddings")
ner = MedicalNerModel.pretrained("ner_oncology_emb_clinical_medium", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverterInternal() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
ner_converter])
data = spark.createDataFrame([["""The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."""]]).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_medium", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_oncology_emb_clinical_medium", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.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 had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-------------------+-----+---+---------------------+
|chunk |begin|end|ner_label |
+-------------------+-----+---+---------------------+
|left |31 |34 |Direction |
|mastectomy |36 |45 |Cancer_Surgery |
|axillary lymph node|54 |72 |Site_Lymph_Node |
|dissection |74 |83 |Cancer_Surgery |
|left |91 |94 |Direction |
|breast cancer |96 |108|Cancer_Dx |
|twenty years ago |110 |125|Relative_Date |
|tumor |132 |136|Tumor_Finding |
|positive |142 |149|Biomarker_Result |
|ER |155 |156|Biomarker |
|PR |162 |163|Response_To_Treatment|
|radiotherapy |183 |194|Radiotherapy |
|breast |229 |234|Site_Breast |
|cancer |241 |246|Cancer_Dx |
|recurred |248 |255|Response_To_Treatment|
|right |262 |266|Direction |
|lung |268 |271|Site_Lung |
|metastasis |273 |282|Metastasis |
|13 years later |284 |297|Relative_Date |
|adriamycin |346 |355|Chemotherapy |
|60 mg/m2 |358 |365|Chemotherapy |
|cyclophosphamide |372 |387|Chemotherapy |
|600 mg/m2 |390 |398|Dosage |
|six courses |406 |416|Cycle_Count |
|first line |422 |431|Line_Of_Therapy |
+-------------------+-----+---+---------------------+
Model Information
| Model Name: | ner_oncology_emb_clinical_medium |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 15.4 MB |
Benchmarking
label tp fp fn total precision recall f1
Histological_Type 138.0 27.0 73.0 211.0 0.8364 0.654 0.734
Direction 679.0 163.0 152.0 831.0 0.8064 0.8171 0.8117
Staging 112.0 24.0 26.0 138.0 0.8235 0.8116 0.8175
Cancer_Score 9.0 2.0 12.0 21.0 0.8182 0.4286 0.5625
Imaging_Test 759.0 132.0 141.0 900.0 0.8519 0.8433 0.8476
Cycle_Number 43.0 29.0 17.0 60.0 0.5972 0.7167 0.6515
Tumor_Finding 971.0 98.0 108.0 1079.0 0.9083 0.8999 0.9041
Site_Lymph_Node 210.0 80.0 61.0 271.0 0.7241 0.7749 0.7487
Invasion 146.0 33.0 21.0 167.0 0.8156 0.8743 0.8439
Response_To_Treat... 224.0 98.0 146.0 370.0 0.6957 0.6054 0.6474
Smoking_Status 39.0 14.0 9.0 48.0 0.7358 0.8125 0.7723
Cycle_Count 113.0 34.0 31.0 144.0 0.7687 0.7847 0.7766
Tumor_Size 203.0 44.0 35.0 238.0 0.8219 0.8529 0.8371
Adenopathy 32.0 12.0 11.0 43.0 0.7273 0.7442 0.7356
Age 203.0 20.0 25.0 228.0 0.9103 0.8904 0.9002
Biomarker_Result 537.0 117.0 148.0 685.0 0.8211 0.7839 0.8021
Unspecific_Therapy 107.0 32.0 67.0 174.0 0.7698 0.6149 0.6837
Site_Breast 95.0 17.0 15.0 110.0 0.8482 0.8636 0.8559
Chemotherapy 684.0 72.0 58.0 742.0 0.9048 0.9218 0.9132
Targeted_Therapy 170.0 31.0 36.0 206.0 0.8458 0.8252 0.8354
Radiotherapy 141.0 43.0 20.0 161.0 0.7663 0.8758 0.8174
Performance_Status 20.0 12.0 12.0 32.0 0.625 0.625 0.625
Pathology_Test 359.0 159.0 127.0 486.0 0.6931 0.7387 0.7151
Site_Other_Body_Part 744.0 338.0 394.0 1138.0 0.6876 0.6538 0.6703
Cancer_Surgery 380.0 83.0 113.0 493.0 0.8207 0.7708 0.795
Line_Of_Therapy 38.0 7.0 10.0 48.0 0.8444 0.7917 0.8172
Pathology_Result 124.0 144.0 217.0 341.0 0.4627 0.3636 0.4072
Hormonal_Therapy 96.0 13.0 27.0 123.0 0.8807 0.7805 0.8276
Site_Bone 167.0 50.0 56.0 223.0 0.7696 0.7489 0.7591
Immunotherapy 61.0 13.0 21.0 82.0 0.8243 0.7439 0.7821
Biomarker 681.0 88.0 150.0 831.0 0.8856 0.8195 0.8513
Cycle_Day 85.0 43.0 43.0 128.0 0.6641 0.6641 0.6641
Frequency 200.0 40.0 35.0 235.0 0.8333 0.8511 0.8421
Route 98.0 13.0 18.0 116.0 0.8829 0.8448 0.8634
Duration 195.0 57.0 101.0 296.0 0.7738 0.6588 0.7117
Death_Entity 40.0 9.0 4.0 44.0 0.8163 0.9091 0.8602
Metastasis 335.0 34.0 27.0 362.0 0.9079 0.9254 0.9166
Site_Liver 146.0 64.0 28.0 174.0 0.6952 0.8391 0.7604
Cancer_Dx 722.0 96.0 108.0 830.0 0.8826 0.8699 0.8762
Grade 55.0 19.0 11.0 66.0 0.7432 0.8333 0.7857
Date 403.0 16.0 14.0 417.0 0.9618 0.9664 0.9641
Site_Lung 341.0 151.0 61.0 402.0 0.6931 0.8483 0.7629
Site_Brain 184.0 82.0 22.0 206.0 0.6917 0.8932 0.7797
Relative_Date 365.0 249.0 95.0 460.0 0.5945 0.7935 0.6797
Race_Ethnicity 47.0 2.0 8.0 55.0 0.9592 0.8545 0.9038
Gender 1260.0 15.0 2.0 1262.0 0.9882 0.9984 0.9933
Dosage 425.0 76.0 60.0 485.0 0.8483 0.8763 0.8621
Oncogene 178.0 89.0 57.0 235.0 0.6667 0.7574 0.7092
Radiation_Dose 41.0 6.0 11.0 52.0 0.8723 0.7885 0.8283
macro - - - - - - 0.7859
micro - - - - - - 0.8130