Using NLP to identify Adverse Drug Events (ADEs)

An adverse drug event (ADE) is defined as harm experienced by a patient as a result of exposure to a medication. A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that usually can only be explored by human readers due to their unstructured nature. In this tutorial we will train a Natural Language Processing (NLP) model to identify ADEs in a given text. We will use an <a href="https://huggingface.co/datasets/ade_corpus_v2" rel="noopener ugc nofollow" target="_blank">ADE dataset</a> from the Hugging Face Dataset Hub to teach the model the difference between ADE-related and non-ADE-related texts. We will use Hugging Face and Amazon SageMaker to train and deploy the model and test it with phrases of our own. <a href="https://towardsdatascience.com/using-nlp-to-identify-adverse-drug-events-ades-7a0194f1966a">Read More</a>