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 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", "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 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", "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 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)
import nlu
nlu.load("en.med_ner.oncology").predict("""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.""")
Results
| chunk | ner_label |
|:-------------------------------|:----------------------|
| left | Direction |
| mastectomy | Cancer_Surgery |
| axillary lymph node dissection | Cancer_Surgery |
| left | Direction |
| breast cancer | Cancer_Dx |
| twenty years ago | Relative_Date |
| tumor | Tumor_Finding |
| positive | Biomarker_Result |
| ER | Biomarker |
| PR | Biomarker |
| radiotherapy | Radiotherapy |
| breast | Site_Breast |
| cancer | Cancer_Dx |
| recurred | Response_To_Treatment |
| right | Direction |
| lung | Site_Lung |
| metastasis | Metastasis |
| 13 years later | Relative_Date |
| adriamycin | Chemotherapy |
| 60 mg/m2 | Dosage |
| cyclophosphamide | Chemotherapy |
| 600 mg/m2 | Dosage |
| six courses | Cycle_Count |
| first line | Line_Of_Therapy |
Model Information
| Model Name: | ner_oncology |
| Compatibility: | Healthcare NLP 4.2.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 34.6 MB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Histological_Type 339 75 114 453 0.82 0.75 0.78
Direction 832 163 152 984 0.84 0.85 0.84
Staging 229 31 29 258 0.88 0.89 0.88
Cancer_Score 37 8 25 62 0.82 0.60 0.69
Imaging_Test 2027 214 177 2204 0.90 0.92 0.91
Cycle_Number 73 29 24 97 0.72 0.75 0.73
Tumor_Finding 1114 64 143 1257 0.95 0.89 0.91
Site_Lymph_Node 491 53 60 551 0.90 0.89 0.90
Invasion 158 36 23 181 0.81 0.87 0.84
Response_To_Treatment 431 149 165 596 0.74 0.72 0.73
Smoking_Status 66 18 2 68 0.79 0.97 0.87
Tumor_Size 1050 112 79 1129 0.90 0.93 0.92
Cycle_Count 177 62 53 230 0.74 0.77 0.75
Adenopathy 67 12 29 96 0.85 0.70 0.77
Age 930 33 19 949 0.97 0.98 0.97
Biomarker_Result 1160 169 285 1445 0.87 0.80 0.84
Unspecific_Therapy 198 86 80 278 0.70 0.71 0.70
Site_Breast 125 15 22 147 0.89 0.85 0.87
Chemotherapy 814 55 65 879 0.94 0.93 0.93
Targeted_Therapy 195 27 33 228 0.88 0.86 0.87
Radiotherapy 276 29 34 310 0.90 0.89 0.90
Performance_Status 121 17 14 135 0.88 0.90 0.89
Pathology_Test 888 296 162 1050 0.75 0.85 0.79
Site_Other_Body_Part 909 275 592 1501 0.77 0.61 0.68
Cancer_Surgery 693 119 126 819 0.85 0.85 0.85
Line_Of_Therapy 101 11 5 106 0.90 0.95 0.93
Pathology_Result 655 279 487 1142 0.70 0.57 0.63
Hormonal_Therapy 169 4 16 185 0.98 0.91 0.94
Site_Bone 264 81 49 313 0.77 0.84 0.80
Biomarker 1259 238 256 1515 0.84 0.83 0.84
Immunotherapy 103 47 25 128 0.69 0.80 0.74
Cycle_Day 200 36 48 248 0.85 0.81 0.83
Frequency 354 27 73 427 0.93 0.83 0.88
Route 91 15 22 113 0.86 0.81 0.83
Duration 625 161 136 761 0.80 0.82 0.81
Death_Entity 34 2 4 38 0.94 0.89 0.92
Metastasis 353 18 17 370 0.95 0.95 0.95
Site_Liver 189 64 45 234 0.75 0.81 0.78
Cancer_Dx 1301 103 93 1394 0.93 0.93 0.93
Grade 190 27 46 236 0.88 0.81 0.84
Date 807 21 24 831 0.97 0.97 0.97
Site_Lung 469 110 90 559 0.81 0.84 0.82
Site_Brain 221 64 58 279 0.78 0.79 0.78
Relative_Date 1211 401 111 1322 0.75 0.92 0.83
Race_Ethnicity 57 8 5 62 0.88 0.92 0.90
Gender 1247 17 7 1254 0.99 0.99 0.99
Oncogene 345 83 104 449 0.81 0.77 0.79
Dosage 900 30 160 1060 0.97 0.85 0.90
Radiation_Dose 108 5 18 126 0.96 0.86 0.90
macro_avg 24653 3999 4406 29059 0.85 0.84 0.84
micro_avg 24653 3999 4406 29059 0.86 0.85 0.85