Machine learning to predict hemorrhage and thrombosis during extracorporeal membrane oxygenation

Excerpt

Hemorrhage and thrombosis are major causes of morbidity and mortality during extracorporeal membrane oxygenation (ECMO). Even in a controlled setting, bleeding occurs frequently—almost half (46%) of the patients randomized to ECMO in the EOLIA trial had hemorrhage requiring transfusion [1]. The pathophysiology of these complications during ECMO is complex, dynamic and not fully understood [2]. This may explain why standard approaches to monitor coagulation are imperfect and studies that employ traditional biostatistical methods do not consistently identify common risk factors. We applied machine learning to an ECMO dataset to predict hemorrhage and thrombosis. Our hypothesis was that machine learning would accurately predict these events and identify novel factors not anticipated clinically or identified by traditional biostatistical methods. …