Description
This model extracts entities related to oncology therapies using granular labels, including mentions of treatments, posology information, and line of therapy. It is the version of ner_oncology_therapy model augmented with langtest library.
Definitions of Predicted Entities:
Cancer_Surgery: Terms that indicate surgery as a form of cancer treatment.Chemotherapy: Mentions of chemotherapy drugs, or unspecific words such as “chemotherapy”.Cycle_Count: 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”).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”).Hormonal_Therapy: Mentions of hormonal drugs used to treat cancer, or unspecific words such as “hormonal therapy”.Immunotherapy: Mentions of immunotherapy drugs, or unspecific words such as “immunotherapy”.Line_Of_Therapy: Explicit references to the line of therapy of an oncological therapy (e.g. “first-line treatment”).Radiotherapy: Terms that indicate the use of Radiotherapy.Radiation_Dose: Dose used in radiotherapy.Response_To_Treatment: Terms related to the clinical progress of the patient related to cancer treatment, including “recurrence”, “bad response” or “improvement”.Route: Words indicating the type of administration route (such as “PO” or “transdermal”).Targeted_Therapy: Mentions of targeted therapy drugs, or unspecific words such as “targeted therapy”.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”).
| test_type | before fail_count | after fail_count | before pass_count | after pass_count | minimum pass_rate | before pass_rate | after pass_rate |
|---|---|---|---|---|---|---|---|
| add_ocr_typo | 725 | 279 | 617 | 1063 | 70% | 46% | 79% |
| add_typo | 248 | 152 | 1066 | 1169 | 70% | 81% | 88% |
| lowercase | 214 | 152 | 1152 | 1214 | 70% | 84% | 89% |
| strip_all_punctuation | 223 | 228 | 1128 | 1123 | 70% | 83% | 83% |
| strip_punctuation | 50 | 37 | 1270 | 1283 | 70% | 96% | 97% |
| titlecase | 764 | 288 | 603 | 1079 | 70% | 44% | 79% |
| uppercase | 1320 | 233 | 48 | 1135 | 70% | 4% | 83% |
| weighted average | 2571 | 938 | 4201 | 5834 | 70% | 62.03% | 86.15% |
Predicted Entities
Cancer_Surgery, Chemotherapy, Cycle_Count, Cycle_Day, Cycle_Number, Dosage, Duration, Frequency, Hormonal_Therapy, Immunotherapy, Line_Of_Therapy, Radiotherapy, Radiation_Dose, Response_To_Treatment, Route, Targeted_Therapy, Unspecific_Therapy
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_therapy_langtest", "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 her 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_therapy_langtest", "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 her 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 |ner_label |
+------------------------------+---------------------+
|mastectomy |Cancer_Surgery |
|axillary lymph node dissection|Cancer_Surgery |
|PR |Response_To_Treatment|
|radiotherapy |Radiotherapy |
|recurred |Response_To_Treatment|
|adriamycin |Chemotherapy |
|60 mg/m2 |Chemotherapy |
|cyclophosphamide |Chemotherapy |
|600 mg/m2 |Chemotherapy |
|six courses |Cycle_Count |
|first line |Line_Of_Therapy |
+------------------------------+---------------------+
Model Information
| Model Name: | ner_oncology_therapy_langtest |
| Compatibility: | Healthcare NLP 5.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.7 MB |
References
In-house annotated oncology case reports.
Benchmarking
label precision recall f1-score support
Cancer_Surgery 0.85 0.85 0.85 490
Chemotherapy 0.92 0.93 0.92 637
Cycle_Count 0.78 0.85 0.82 128
Cycle_Day 0.66 0.71 0.68 68
Cycle_Number 0.75 0.58 0.65 52
Dosage 0.91 0.91 0.91 311
Duration 0.80 0.74 0.77 221
Frequency 0.86 0.89 0.88 162
Hormonal_Therapy 0.91 0.90 0.91 92
Immunotherapy 0.82 0.79 0.81 63
Line_Of_Therapy 0.78 0.78 0.78 46
Radiation_Dose 0.88 0.88 0.88 48
Radiotherapy 0.83 0.88 0.85 154
Response_To_Treatment 0.71 0.68 0.69 377
Route 0.94 0.85 0.89 92
Targeted_Therapy 0.90 0.89 0.89 166
Unspecific_Therapy 0.74 0.79 0.77 150
micro-avg 0.84 0.84 0.84 3257
macro-avg 0.83 0.82 0.82 3257
weighted-avg 0.84 0.84 0.84 3257