Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot
Rushad Patell,
Jeffrey I. Zwicker,
Rohan Singh
et al.
Abstract:The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review pap… Show more
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