Description
This dedicated pipeline extracts clinical terms and utilizes sbiobert_base_cased_mli Sentence Bert Embeddings to link them to their corresponding MedDRA PT (Preferred Term) codes. Additionally, it provides the MedDRA Preferred Term codes for each MedDRA PT code within the meddra_pt_code metadata’s all_k_aux_labels. Furthermore, it conducts mappings of MedDRA PT codes to MedDRA Lowest Level Term (LLT) codes using the meddra_pt_llt_mapper model and to ICD-10 codes using the meddra_pt_icd10_mapper model.
This pipeline can extract the following clincial entities:
Procedure, Kidney_Disease, Cerebrovascular_Disease, Heart_Disease, Disease_Syndrome_Disorder, ImagingFindings, Symptom, VS_Finding, EKG_Findings, Communicable_Disease, Substance, Internal_organ_or_component, External_body_part_or_region, Modifier, Triglycerides, Alcohol, Smoking, Pregnancy, Hypertension, Obesity, Injury_or_Poisoning, Test, Hyperlipidemia, BMI, Oncological, Psychological_Condition, LDL, Diabetes, PROBLEM.
How to use
from sparknlp.pretrained import PretrainedPipeline
meddra_pt_pipeline = PretrainedPipeline.from_disk("meddra_pt_resolver_pipeline")
result = meddra_pt_pipeline.fullAnnotate("""This is an 82-year-old male with a history of prior tobacco use, benign hypertension, chronic renal insufficiency, chronic bronchitis, gastritis, and ischemic attack. He initially presented to Braintree with ST elevation and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of the left main coronary artery stenosis, which was complicated by hypotension and bradycardia. We describe the side effects of 5-FU in a colon cancer patient who suffered mucositis and dermatitis.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val meddra_pt_pipeline = PretrainedPipeline.from_disk("meddra_pt_resolver_pipeline")
val result = meddra_pt_pipeline.fullAnnotate("""This is an 82-year-old male with a history of prior tobacco use, benign hypertension, chronic renal insufficiency, chronic bronchitis, gastritis, and ischemic attack. He initially presented to Braintree with ST elevation and was transferred to St. Margaret’s Center. He underwent cardiac catheterization because of the left main coronary artery stenosis, which was complicated by hypotension and bradycardia. We describe the side effects of 5-FU in a colon cancer patient who suffered mucositis and dermatitis.""")
Results
+--------------------------------------+-------------------------+--------------+--------------------------+-------------------------------------------------+-----------------------------------+
|chunk |label |meddra_pt_code|resolution |icd10_code |meddra_llt_code |
+--------------------------------------+-------------------------+--------------+--------------------------+-------------------------------------------------+-----------------------------------+
|tobacco |Smoking |10067622 |tobacco interaction |NONE |10067622:Tobacco interaction |
|benign hypertension |PROBLEM |10049079 |labile hypertension |NONE |10049079:Labile hypertension |
|chronic renal insufficiency |Kidney_Disease |10038435 |renal failure |N19:Unspecified kidney failure |10016149:Failure kidney |
|chronic bronchitis |PROBLEM |10006458 |bronchitis chronic |J41.0:Simple chronic bronchitis |10003568:Asthmatoid bronchitis |
|gastritis |Disease_Syndrome_Disorder|10017853 |gastritis |K29:Gastritis and duodenitis |10000769:Acute gastritis |
|ischemic attack |Cerebrovascular_Disease |10061216 |infarction |NONE |10021762:Infarction NOS |
|ST elevation |PROBLEM |10049785 |atrial pressure increased |NONE |10049785:Atrial pressure increased |
|cardiac catheterization |Procedure |10007815 |catheterisation cardiac |NONE |10007527:Cardiac catheterisation |
|the left main coronary artery stenosis|PROBLEM |10011089 |coronary artery stenosis |NONE |10011089:Coronary artery stenosis |
|hypotension |VS_Finding |10021097 |hypotension |I95:Hypotension |10005753:Blood pressure low |
|bradycardia |VS_Finding |10006093 |bradycardia |R00.1:Bradycardia, unspecified |10006093:Bradycardia |
|the side effects |PROBLEM |10054126 |post procedural discomfort|NONE |10054126:Post procedural discomfort|
|a colon cancer |PROBLEM |10009944 |colon cancer |C18:Malignant neoplasm of colon |10006903:Caecal cancer |
|mucositis |Disease_Syndrome_Disorder|10090972 |immune-mediated mucositis |NONE |10090972:Immune-mediated mucositis |
|dermatitis |Disease_Syndrome_Disorder|10012431 |dermatitis |L27:Dermatitis due to substances taken internally|10000593:Acrodermatitis |
+--------------------------------------+-------------------------+--------------+--------------------------+-------------------------------------------------+-----------------------------------+
Model Information
| Model Name: | meddra_pt_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 2.2 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMergeModel
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- Resolution2Chunk
- ChunkMapperModel
- ChunkMapperModel
References
This pipeline is prepared using the models that are trained with the September 2024 (v27.1) of MedDRA dataset. To utilize this pipeline, possession of a valid MedDRA license is requisite. If you possess one and wish to use this model, kindly contact us at support@johnsnowlabs.com.