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Embrace the phenotype with NLP to drive precision medicine

Healthcare systems are producing data at a rate that exceeds growth in any other industry. A huge amount (80%) of this data is unstructured.

Those who adopt technologies to untap the potential value of these data are best placed to understand their populations more. And are able to provide appropriate and tailored treatment and initiatives where they are needed.

The latest in AIMed’s webinar series explores how leaders from Washington University School of Medicine, St Louis and Kaiser Permanente Northern California are using NLP to advance clinical care.

Data and AI now have leading roles to play in advancing precision medicine research by identifying early onset and treatments for disease. Dr Philip Payne shares how Washington University is building a set of NLP pipelines to extract high quality phenotypic data from the clinical narrative, to develop registries for patients with Alzheimer’s disease, breast cancer, diabetes and obesity. The use of natural language processing is key because close to 80% of the high value phenotypic data is encoded in the clinical narrative, not in structured or discrete fields in the electronic health record.

Dr Matthew Solomon presents how Kaiser Permanente Northern California, a large, integrated healthcare system, developed and validated NLP queries to identify aortic stenosis (AS) cases and associated parameters from semi-structured echocardiogram reports and compared its accuracy to administrative diagnosis codes. This use case gives a brief overview of the results and how NLP algorithms were substantially more accurate than diagnosis codes for identifying AS, provided richer clinical detail on ascertained cases, and will serve as a platform for population management strategies.

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