NLP Summit 2021: Extraction and validation of biomarker testing data from lab reports - Michael Ayars
The advent of precision medicine has rapidly multiplied the targets, methodologies, and providers available to clinicians in biomarker testing. There is a growing demand for real world data (RWD) to identify the best testing practices and improve patient outcomes. Much of this data remains confined to scanned documents that conventionally have required costly and time-consuming manual review to extract.
Advancements in optical character recognition (OCR) and natural language processing (NLP) have opened the door to automatically extracting this data at a scale previously out-of-reach. To explore these new possibilities, this study used OCR and NLP tools to extract and structure the biomarker testing history of non-small cell lung cancer (NSCLC) patients from scanned lab reports.
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