Oncology - Clinical NLP Demos & Notebooks

This demo shows how oncological terms can be detected using Spark NLP Healthcare NER, Assertion Status, and Relation Extraction models. (...)

This demo shows how to extract more than 40 Oncology-related entities including those related to Cancer diagnosis, Staging information, Tumors, Lymph Nodes, and Metastases. Also shows how to extract entities related to Oncology Therapies, Mentions of Treatments, posology information, Tumor Size, Cancer Therapies, and anatomical entities using pretrained Spark NLP clinical models. (...)

This demo shows how to extract entities Pathology Tests, Imaging Tests, mentions of Biomarkers, and their results from clinical texts using pretrained Spark NLP clinical models. (...)

This demo shows how to extract Demographic information, Age, Gender, and Smoking status from oncology texts. (...)

This demo shows how to detect the assertion status of entities related to oncology (including diagnoses, therapies, and tests), and if a demographic entity refers to the patient or someone else. (...)

This demo shows how to identify relations between Clinical entities, Tumor mentions, Anatomical entities, Tests, Biomarkers, Anatomical Entities, Tumor Size, Tumor Finding, Date, and their corresponding using pretrained Oncology Relation Extraction (RE) models. (...)

This model maps oncology terminology to ICD-O codes using Entity Resolvers. (...)

This model is a sentence classification system based on BioBERT that is capable of identifying if clinical sentences contain terms associated with biomarkers. (...)

This model is intended to detect oncological responses to treatments in clinical notes. (...)

This demo classifies google reviews of various healthcare facilities. (...)

This demo semantically classifies an article based on its abstract, specifically related to the hallmarks of cancer. (...)