Drugs & Adverse Events - Clinical NLP Demos & Notebooks

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Drugs & Adverse Events - Live Demos & Notebooks

Explore Adverse Drug Events with Spark NLP Models
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. (...)
Detect drugs and prescriptions
Automatically identify Drug, Dosage, Duration, Form, Frequency, Route, and Strength details in clinical documents using three of our pretrained Spark NLP clinical models. (...)
Detect posology relations
Automatically identify relations between drugs, dosage, duration, frequency and strength using our pretrained clinical Relation Extraction (RE) model. (...)
Extract Drugs and Chemicals
This demo shows how Names of Drugs & Chemicals can be detected using a Spark NLP Healthcare NER model. (...)
Identify Relations Between Drugs and Adversary Events
This demo shows how to detect relations between drugs and adverse reactions caused by them. (...)
Extract conditions and benefits from drug reviews
This model shows how to extract conditions and benefits from drug reviews. (...)
Detect Drug Chemicals
Automatically identify drug chemicals in clinical documents using the pretrained Spark NLP clinical models. (...)
Detect ADE-related texts
This model classifies texts as containing or not containing adverse drug events description. (...)
Detect Opioid Related Entities
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. (...)