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
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") \
.setInputCols(["document"]) \
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d", "de", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
ner_model = MedicalNerModel.pretrained("ner_oncology_wip", "de", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
ner_model,
ner_converter])
data = spark.createDataFrame([["""Patientin: 67 Jahre, weiblich
Diagnose: Invasives duktales Mammakarzinom links (G3)
Befunde:
Schmerzloser, tastbarer Knoten im oberen äußeren Quadranten links
Hautrötung, eingezogene Mamille, tastbare axilläre Lymphknoten
Histologie:
ER + , PR + , HER2-negativ, Ki-67 bei 35 %
Bildgebung:
Mammographie: Tumor 2,8 cm, Mikroverkalkungen
MRT: Tumorgröße 3,1 cm mit Drüseninfiltration
PET-CT: Keine Fernmetastasen
Therapie:
Neoadjuvante Chemotherapie
Brusterhaltende Operation mit Sentinel-Lymphknotenbiopsie
Adjuvante Strahlentherapie
Hormontherapie mit Letrozol (5 Jahre)
Beurteilung:
Fortgeschrittenes lokal begrenztes Mammakarzinom, günstiger Hormonrezeptorstatus. Therapie gemäß Tumorkonferenz empfohlen."""]]).toDF("text")
result = nlpPipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx") \
.setInputCols(["document"]) \
.setOutputCol("sentence")
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = nlp.WordEmbeddingsModel.pretrained("w2v_cc_300d", "de", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
ner_model = medical.NerModel.pretrained("ner_oncology_wip", "de", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = medical.NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = nlp.Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
ner_model,
ner_converter])
data = spark.createDataFrame([["""Patientin: 67 Jahre, weiblich
Diagnose: Invasives duktales Mammakarzinom links (G3)
Befunde:
Schmerzloser, tastbarer Knoten im oberen äußeren Quadranten links
Hautrötung, eingezogene Mamille, tastbare axilläre Lymphknoten
Histologie:
ER + , PR + , HER2-negativ, Ki-67 bei 35 %
Bildgebung:
Mammographie: Tumor 2,8 cm, Mikroverkalkungen
MRT: Tumorgröße 3,1 cm mit Drüseninfiltration
PET-CT: Keine Fernmetastasen
Therapie:
Neoadjuvante Chemotherapie
Brusterhaltende Operation mit Sentinel-Lymphknotenbiopsie
Adjuvante Strahlentherapie
Hormontherapie mit Letrozol (5 Jahre)
Beurteilung:
Fortgeschrittenes lokal begrenztes Mammakarzinom, günstiger Hormonrezeptorstatus. Therapie gemäß Tumorkonferenz empfohlen."""]]).toDF("text")
result = nlpPipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val embeddings = WordEmbeddingsModel().pretrained("w2v_cc_300d", "de", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_oncology_wip", "de", "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(
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
ner_model,
ner_converter))
val data = Seq("""Patientin: 67 Jahre, weiblich
Diagnose: Invasives duktales Mammakarzinom links (G3)
Befunde:
Schmerzloser, tastbarer Knoten im oberen äußeren Quadranten links
Hautrötung, eingezogene Mamille, tastbare axilläre Lymphknoten
Histologie:
ER + , PR + , HER2-negativ, Ki-67 bei 35 %
Bildgebung:
Mammographie: Tumor 2,8 cm, Mikroverkalkungen
MRT: Tumorgröße 3,1 cm mit Drüseninfiltration
PET-CT: Keine Fernmetastasen
Therapie:
Neoadjuvante Chemotherapie
Brusterhaltende Operation mit Sentinel-Lymphknotenbiopsie
Adjuvante Strahlentherapie
Hormontherapie mit Letrozol (5 Jahre)
Beurteilung:
Fortgeschrittenes lokal begrenztes Mammakarzinom, günstiger Hormonrezeptorstatus. Therapie gemäß Tumorkonferenz empfohlen.""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-------------------------------+-----+---+--------------------+
|chunk |begin|end|ner_label |
+-------------------------------+-----+---+--------------------+
|67 Jahre |11 |18 |Age |
|weiblich |21 |28 |Gender |
|Invasives |41 |49 |Histological_Type |
|duktales |51 |58 |Histological_Type |
|Mammakarzinom links |60 |78 |Cancer_Dx |
|Knoten |120 |125|Tumor_Finding |
|oberen äußeren Quadranten links|130 |160|Direction |
|Mamille |186 |192|Site_Other_Body_Part|
|axilläre Lymphknoten |204 |223|Site_Lymph_Node |
|Histologie |225 |234|Pathology_Test |
|ER |238 |239|Biomarker |
|+ |241 |241|Biomarker_Result |
|PR |245 |246|Biomarker |
|+ |248 |248|Biomarker_Result |
|HER2-negativ |252 |263|Biomarker |
|Ki-67 |266 |270|Biomarker |
|35 % |276 |279|Biomarker_Result |
|Bildgebung |281 |290|Imaging_Test |
|Mammographie |294 |305|Imaging_Test |
|Tumor |308 |312|Tumor_Finding |
|2,8 cm |314 |319|Tumor_Size |
|MRT |340 |342|Imaging_Test |
|Tumorgröße |345 |354|Tumor_Finding |
|3,1 cm |356 |361|Tumor_Size |
|Drüseninfiltration |367 |384|Histological_Type |
|PET-CT |386 |391|Imaging_Test |
|Neoadjuvante Chemotherapie |426 |451|Chemotherapy |
|Brusterhaltende Operation |453 |477|Cancer_Surgery |
|Sentinel-Lymphknotenbiopsie |483 |509|Pathology_Test |
|Adjuvante Strahlentherapie |511 |536|Radiotherapy |
|Hormontherapie |538 |551|Hormonal_Therapy |
|Letrozol |557 |564|Hormonal_Therapy |
|Fortgeschrittenes lokal |589 |611|Staging |
|Mammakarzinom |624 |636|Cancer_Dx |
+-------------------------------+-----+---+--------------------+
Model Information
| Model Name: | ner_oncology_wip |
| Compatibility: | Healthcare NLP 5.5.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | de |
| Size: | 3.1 MB |
Benchmarking
label precision recall f1 support
Histological_Type 0.7533 0.689 0.7197 164.0
Direction 0.8469 0.8758 0.8611 499.0
Staging 0.9006 0.9063 0.9034 160.0
Cancer_Score 0.9828 0.8636 0.9194 66.0
Imaging_Test 0.8284 0.8273 0.8278 741.0
Cycle_Number 0.95 0.6552 0.7755 58.0
Tumor_Finding 0.9024 0.8898 0.896 644.0
Site_Lymph_Node 0.8235 0.8434 0.8333 166.0
Invasion 0.8 0.7027 0.7482 74.0
Response_To_Treatment 0.6931 0.6231 0.6563 337.0
Smoking_Status 0.8125 0.6842 0.7429 19.0
Tumor_Size 0.8991 0.9159 0.9074 535.0
Cycle_Count 0.701 0.9408 0.8034 152.0
Adenopathy 0.8919 0.7674 0.825 43.0
Age 0.9086 0.9747 0.9405 316.0
Biomarker_Result 0.852 0.7795 0.8141 635.0
Unspecific_Therapy 0.6707 0.5556 0.6077 99.0
Site_Breast 0.8875 0.8161 0.8503 87.0
Chemotherapy 0.8897 0.9089 0.8992 417.0
Targeted_Therapy 0.8247 0.8247 0.8247 97.0
Radiotherapy 0.8837 0.8172 0.8492 93.0
Performance_Status 0.9302 0.8511 0.8889 47.0
Pathology_Test 0.7832 0.7592 0.771 490.0
Site_Other_Body_Part 0.6967 0.7168 0.7066 625.0
Cancer_Surgery 0.8631 0.8536 0.8583 362.0
Line_Of_Therapy 0.84 0.7 0.7636 30.0
Pathology_Result 0.625 0.6436 0.6341 505.0
Hormonal_Therapy 0.8679 0.7541 0.807 61.0
Site_Bone 0.7273 0.7339 0.7306 109.0
Biomarker 0.8056 0.8781 0.8403 689.0
Immunotherapy 0.9524 0.4545 0.6154 44.0
Cycle_Day 0.6744 0.8467 0.7508 137.0
Route 0.8667 0.8864 0.8764 44.0
Frequency 0.9278 0.8564 0.8907 195.0
Duration 0.7583 0.6223 0.6836 368.0
Death_Entity 0.9231 0.4615 0.6154 26.0
Metastasis 0.9042 0.7438 0.8162 203.0
Site_Liver 0.8675 0.8182 0.8421 88.0
Cancer_Dx 0.9025 0.8597 0.8806 549.0
Grade 0.8015 0.8015 0.8015 136.0
Date 0.9767 0.973 0.9749 518.0
Site_Lung 0.8483 0.8027 0.8249 223.0
Site_Brain 0.8286 0.5686 0.6744 102.0
Relative_Date 0.7418 0.8146 0.7765 642.0
Race_Ethnicity 0.8621 0.8333 0.8475 30.0
Gender 0.8953 0.9265 0.9106 517.0
Dosage 0.8859 0.8787 0.8823 371.0
Oncogene 0.785 0.7904 0.7877 291.0
Radiation_Dose 0.9273 0.9107 0.9189 56.0
macro-avg - - 0.8077 -
micro-avg - - 0.8218 -