Automatically detect clinical entities using our different NER deep learning models. (...)

Automatically detect more than 50 clinical entities using our NER deep learning model. (...)

This demo automatically identify the entities of Problem, Test, and Treatment Entities in medical texts. (...)

In this demo, We are showing the results of the Original Model on the original text and on the corruped text. In addition, We are showing the result of the Langtest model which is augmented with langtest library for corrupted text cases. (...)

Automatically detect if a diagnosis or a symptom is present, absent, uncertain or associated to other persons (e.g. family members). (...)

Automatically identify diagnoses and procedures in clinical documents using the pretrained Spark NLP clinical models. (...)

Automatically identify three types of relations between clinical events: After, Before and Overlap using our pretrained clinical Relation Extraction (RE) model. (...)

Automatically identify relations between symptoms and treatment using our pretrained clinical Relation Extraction (RE) model. (...)

Use pre-trained relation extraction models to extract relations between body parts and clinical entities. (...)

Detect clinical entities such as problems, tests and treatments, and how they relate to specific dates. (...)

This pipeline can be used to explore all the available pretrained NER models at once. When you run this pipeline over your text, you will end up with the predictions coming out of each pretrained clinical NER model. (...)