A machine learning model using PT-INR and aPTT accurately classified vitamin K antagonist and direct oral anticoagulant exposure to assist emergency decision-making in acute stroke care.
Does a machine learning model using PT-INR and aPTT accurately predict oral anticoagulant use in patients with acute stroke?
A simple machine learning model using standard coagulation labs can accurately detect oral anticoagulant use in acute stroke patients, aiding emergency decision-making when medication history is unknown.
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A simple machine learning model using only prothrombin time-international normalized ratio and activated partial thromboplastin time classified vitamin K antagonist and direct oral anticoagulant exposure with high accuracy and practical clinical utility. This tool assists emergency decision-making when medication history is unknown, supporting the safety and timeliness of acute stroke care.
Fujiwara et al. (Wed,) reported a other. A machine learning model using PT-INR and aPTT accurately classified vitamin K antagonist and direct oral anticoagulant exposure to assist emergency decision-making in acute stroke care.