AMIA Annu Symp Proc - Learning to predict post-hospitalization VTE risk from EHR data.

Tópicos

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Resumo

We consider the task of predicting which patients are most at risk for post-hospitalization venothromboembolism (VTE) using information automatically elicited from an EHR. Given a set of cases and controls, we use machine-learning methods to induce models for making these predictions. Our empirical evaluation of this approach offers a number of interesting and important conclusions. We identify several risk factors for VTE that were not previously recognized. We show that machine-learning methods are able to induce models that identify high-risk patients with accuracy that exceeds previously developed scoring models for VTE. Additionally, we show that, even without having prior knowledge about relevant risk factors, we are able to learn accurate models for this task.

Resumo Limpo

consid task predict patient risk posthospit venothromboembol vte use inform automat elicit ehr given set case control use machinelearn method induc model make predict empir evalu approach offer number interest import conclus identifi sever risk factor vte previous recogn show machinelearn method abl induc model identifi highrisk patient accuraci exceed previous develop score model vte addit show even without prior knowledg relev risk factor abl learn accur model task

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