About this Video
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 a long time range. More than half of the clinically relevant data for applications like recommending a course of treatment for a patient, finding actionable genomic biomarkers, matching patients to clinical trials, matching patients to research results, or curating real-world data is only found in free-text data. This session describes some of the first real-world projects that power such applications, extracting clinical facts using Spark NLP for Healthcare - the most widely used, most accurate, and most scalable Healthcare NLP library today - along with lessons learned and promising future directions. Featuring David Talby, cShow Morehief technology officer at John Snow Labs. Show LessAccelerating 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 a long time range. More than half of the clinically relevant data for applications like recommending a course of treatment for a patient, finding actionable genomic biomarkers, matching patients to clinical trials, matching patients to research results, or curating real-world data is only found in free-text data. This session describes some of the first real-world projects that power such applications, extracting clinical facts using Spark NLP for Healthcare - the most widely used, most accurate, and most scalable Healthcare NLP library today - along with lessons learned and promising future directions. Featuring David Talby, c