Automated Summarization of Clinical Notes

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In this webinar, Veysel will delve into the challenges of and need for text summarization and the importance of summarization in various domains, especially in healthcare. He will cover various techniques for text summarization, ranging from classical methods to cutting-edge approaches such as LLMs, extractive and abstractive summarization.
Veysel will then introduce you to the new clinical text summarization module in Spark NLP for Healthcare library, which offers state-of-the-art features and capabilities, based on one of the latest LLM architectures. This module has been trained and designed to cater specifically to the needs of the healthcare domain, enabling it to summarize clinical notes with high accuracy and efficiency.
Veysel will also have a hands-on session where you will get the chance to work with clinical text summarization models, use cases, and live examples.
Veysel Kocaman - Head of Data Science at John Snow Labs
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[@teachingmachine]

It's all about drafting proper prompt Cocaman.. anyways thanks for the information, I will try to regenerate these summarisations on the models shown with improvised prompts.

[@encapsulatio]

But how can you know for sure it won't exclude important info in a summarization?

[@MrXxXx420]

the text size in these examples are relatively shorter. in real world setting, they are really long. For example, notes for a patient with 7 years history of Chronic liver disease. How do you guys deal with long text size?

[@hopehope9]

I am a researcher and my topic is “ Improved clinical notes summarization” what is your suggustion for the models and dataset which is give a rubost effect in my research ?