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Applying State-of-the-art Natural Language Processing for Personalized Healthcare

Accelerating progress in personalized healthcare requires learning the causal relationships between diseases, genes, treatments, medications, labs, and other clinical information – at scale over a large population and time range. More than half of the clinically relevant data in oncology is only found in free-text pathology reports, radiology reports, sequencing reports, and progress notes.

Extracting and normalizing these facts from these clinical documents requires training oncology-specific models that can accurately extract these specific facts from a variety of documents. This talk describes results and lessons learned, from a real-world project doing this at scale.

SelectData interprets millions of patient stories with deep learned OCR and NLP

Many businesses still depend on documents stored as images—from receipts, manifests, invoices, medical reports, and ID cards snapped with mobile phone cameras to...