Description
Important Note: This model is trained with a partial dataset that is used to train ner_oncology; and meant to be used for benchmarking run at LLMs Healthcare Benchmarks.
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", "en", "clinical/models")\
.setInputCols(["sentence", "token"]) \
.setOutputCol("embeddings")
ner = MedicalNerModel.pretrained("ner_oncology_limited_80p_for_benchmarks", "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", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_oncology_limited_80p_for_benchmarks", "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 dissection|54 |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|Biomarker |
|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|Dosage |
|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_limited_80p_for_benchmarks |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 15.3 MB |
Benchmarking
label tp fp fn total precision recall f1
Histological_Type 135.0 60.0 68.0 203.0 0.6923 0.665 0.6784
Direction 642.0 170.0 149.0 791.0 0.7906 0.8116 0.801
Staging 122.0 20.0 29.0 151.0 0.8592 0.8079 0.8328
Cancer_Score 14.0 8.0 8.0 22.0 0.6364 0.6364 0.6364
Imaging_Test 743.0 197.0 155.0 898.0 0.7904 0.8274 0.8085
Cycle_Number 45.0 34.0 10.0 55.0 0.5696 0.8182 0.6716
Tumor_Finding 948.0 104.0 110.0 1058.0 0.9011 0.896 0.8986
Site_Lymph_Node 199.0 62.0 57.0 256.0 0.7625 0.7773 0.7698
Invasion 116.0 33.0 20.0 136.0 0.7785 0.8529 0.814
Response_To_Treat... 244.0 145.0 144.0 388.0 0.6272 0.6289 0.6281
Smoking_Status 54.0 7.0 2.0 56.0 0.8852 0.9643 0.9231
Cycle_Count 96.0 26.0 32.0 128.0 0.7869 0.75 0.768
Tumor_Size 205.0 43.0 49.0 254.0 0.8266 0.8071 0.8167
Adenopathy 29.0 6.0 4.0 33.0 0.8286 0.8788 0.8529
Age 212.0 14.0 18.0 230.0 0.9381 0.9217 0.9298
Biomarker_Result 593.0 138.0 122.0 715.0 0.8112 0.8294 0.8202
Unspecific_Therapy 124.0 47.0 50.0 174.0 0.7251 0.7126 0.7188
Site_Breast 96.0 13.0 14.0 110.0 0.8807 0.8727 0.8767
Chemotherapy 570.0 40.0 65.0 635.0 0.9344 0.8976 0.9157
Targeted_Therapy 173.0 11.0 17.0 190.0 0.9402 0.9105 0.9251
Radiotherapy 128.0 26.0 21.0 149.0 0.8312 0.8591 0.8449
Performance_Status 29.0 10.0 14.0 43.0 0.7436 0.6744 0.7073
Pathology_Test 395.0 157.0 105.0 500.0 0.7156 0.79 0.751
Site_Other_Body_Part 682.0 284.0 370.0 1052.0 0.706 0.6483 0.6759
Cancer_Surgery 388.0 100.0 75.0 463.0 0.7951 0.838 0.816
Line_Of_Therapy 30.0 9.0 8.0 38.0 0.7692 0.7895 0.7792
Pathology_Result 135.0 162.0 169.0 304.0 0.4545 0.4441 0.4493
Hormonal_Therapy 95.0 9.0 10.0 105.0 0.9135 0.9048 0.9091
Site_Bone 171.0 42.0 68.0 239.0 0.8028 0.7155 0.7566
Immunotherapy 57.0 31.0 13.0 70.0 0.6477 0.8143 0.7215
Biomarker 759.0 161.0 118.0 877.0 0.825 0.8655 0.8447
Cycle_Day 84.0 32.0 32.0 116.0 0.7241 0.7241 0.7241
Frequency 199.0 33.0 32.0 231.0 0.8578 0.8615 0.8596
Route 88.0 12.0 23.0 111.0 0.88 0.7928 0.8341
Duration 184.0 60.0 110.0 294.0 0.7541 0.6259 0.684
Death_Entity 36.0 3.0 2.0 38.0 0.9231 0.9474 0.9351
Metastasis 307.0 18.0 21.0 328.0 0.9446 0.936 0.9403
Site_Liver 138.0 46.0 35.0 173.0 0.75 0.7977 0.7731
Cancer_Dx 720.0 112.0 100.0 820.0 0.8654 0.878 0.8717
Grade 48.0 21.0 13.0 61.0 0.6957 0.7869 0.7385
Date 365.0 17.0 16.0 381.0 0.9555 0.958 0.9567
Site_Lung 354.0 100.0 87.0 441.0 0.7797 0.8027 0.7911
Site_Brain 133.0 28.0 59.0 192.0 0.8261 0.6927 0.7535
Relative_Date 365.0 264.0 80.0 445.0 0.5803 0.8202 0.6797
Race_Ethnicity 51.0 10.0 5.0 56.0 0.8361 0.9107 0.8718
Gender 1267.0 15.0 3.0 1270.0 0.9883 0.9976 0.9929
Dosage 337.0 45.0 78.0 415.0 0.8822 0.812 0.8457
Oncogene 135.0 57.0 78.0 213.0 0.7031 0.6338 0.6667
Radiation_Dose 35.0 10.0 6.0 41.0 0.7778 0.8537 0.814
macro - - - - - - 0.7974
micro - - - - - - 0.8154