Machine learning pinpoints trauma patients who benefit most from bleeding-control drug

Approximately 4.5 million people die annually from traumatic injury worldwide, many from preventable blood loss.

Each year, 4.5 million lives end in traumatic injury — many from blood loss that a decades-old drug might have slowed. Researchers at Osaka University have used machine learning to ask a question medicine has long struggled to answer: not whether tranexamic acid works, but for whom. By finding eight distinct patient profiles within a dataset of more than 50,000 cases, they have begun to transform a blunt clinical instrument into something more like a scalpel — a step toward treating the person, not the diagnosis.

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Machine learning pinpoints trauma patients who benefit most from bleeding-control drug

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