AMIA Annu Symp Proc - Predicting Surgical Risk: How Much Data is Enough?

Tópicos

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Resumo

As medicine becomes increasingly data driven, caregivers are required to collect and analyze an increasingly copious volume of patient data. Although methods for studying these data have recently evolved, the collection of clinically validated data remains cumbersome. We explored how to reduce the amount of data needed to risk stratify patients. We focused our investigation on patient data from the National Surgical Quality Improvement Program (NSQIP) to study how the accuracy of predictive models may be affected by changing the number of variables, the categories of variables, and the times at which these variables were collected. By examining the implications of creating predictive models based on the entire variable set in NSQIP and smaller selected variable groups, our results show that using far fewer variables than traditionally done can lead to similar predictive accuracy.

Resumo Limpo

medicin becom increas data driven caregiv requir collect analyz increas copious volum patient data although method studi data recent evolv collect clinic valid data remain cumbersom explor reduc amount data need risk stratifi patient focus investig patient data nation surgic qualiti improv program nsqip studi accuraci predict model may affect chang number variabl categori variabl time variabl collect examin implic creat predict model base entir variabl set nsqip smaller select variabl group result show use far fewer variabl tradit done can lead similar predict accuraci

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