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Presented by:
-Timothy Laurent, Principal Engineer - Machine Learning at Invitae
-Isaac Palka, Senior Director of Engineering at Invitae
In this talk, Timothy Laurent and Isaac Palka will show how Invitae uses NLP to transform unstructured medical documents into structured patient health records. They will introduce LayoutLMv2, an ML model open-sourced by Microsoft, which is trained to interpret document text and layout. They'll discuss how they trained this model on their own corpus of medical records labeled with a custom OCR labeling interface, how they scaled this model in the cloud, and how it fits into larger information extraction workflows (e.g. table comprehension, relation extraction). Finally, they'll show how the extracted entities are leveraged in their proprietary app to construct FHIR resources, and to generate high-grade, large-volume patient records that are searchable and interoperable.
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