Applying Healthcare-Specific LLMs to Build Oncology Patient Timelines&Recommend Clinical Guidelines

Описание: Install NLP Libraries https://www.johnsnowlabs.com/install/
Watch all NLP Summit 2024 sessions: https://www.nlpsummit.org/nlp-summit-...

In this talk, Vishakha covers two case studies. First, she discusses how applying healthcare-specific Large Language Models (LLMs) to Electronic Health Records (EHRs) presents a promising approach to constructing detailed oncology patient timelines. This task involves extracting and synthesizing chemotherapy treatment data from diverse clinical notes, including those from primary care providers, oncologists, discharge summaries, emergency departments, pathology, and radiology reports.
Second, she explores how John Snow Labs’ healthcare-specific Large Language Model (LLM) offers a transformative approach to matching patients with the National Comprehensive Cancer Network (NCCN) clinical guidelines. By analyzing comprehensive patient data, including genetic, epigenetic, and phenotypic information, the LLM accurately aligns individual patient profiles with the most relevant clinical guidelines. This innovation enhances precision in oncology care by ensuring that each patient receives tailored treatment recommendations based on the latest NCCN guidelines.

This session was presented by Vishakha Sharma, Senior Principal Data Scientist at Roche

Connect with us:
Our website: https://www.johnsnowlabs.com/
LinkedIn:   / johnsnowlabs  
Facebook:   / johnsnowlabsinc  
Twitter: @johnsnowlabs