Drugs & Adverse Events - Clinical NLP Demos & Notebooks

This demo shows how detect adverse reactions of drugs in reviews, tweets, and medical text using Spark NLP Healthcare NER, Sequence Classification, Assertion Status, and Relation Extraction models. (...)

This demo includes details about different kinds of pretrained models to detect and label opioid related entities within text data. (...)

Automatically identify Drug, Dosage, Duration, Form, Frequency, Route, and Strength details in clinical documents using three of our pretrained Spark NLP clinical models. (...)

Automatically identify relations between drugs, dosage, duration, frequency and strength using our pretrained clinical Relation Extraction (RE) model. (...)

This demo shows how Names of Drugs & Chemicals can be detected using a Spark NLP Healthcare NER model. (...)

This demo shows how to detect relations between drugs and adverse reactions caused by them. (...)

This model shows how to extract conditions and benefits from drug reviews. (...)

Automatically identify drug chemicals in clinical documents using the pretrained Spark NLP clinical models. (...)

This model classifies texts as containing or not containing adverse drug events description. (...)

This model is designed to detect and label opioid related entities within text data. Opioids are a class of drugs that include the illegal drug heroin, synthetic opioids such as fentanyl, and pain relievers available legally by prescription. The model has been trained using advanced deep learning techniques on a diverse range of text sources and can accurately recognize and classify a wide range of opioid-related entities. (...)