Incomplete electronic health records can exacerbate bias in predictive models
Electronic health records (EHR) data are often incomplete, creating a significant challenge for researchers, and data gaps may be unequally distributed across patient groups: People with less access to care, often people of color or with lower socioeconomic status, tend to have more incomplete EHRs. A new study from Penn LDI finds that predictive models trained using incomplete EHR data performed poorly for patients with lower access to care.
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