Semi-supervised calibration of noisy event risk (SCANER) with electronic health records.

ABSTRACT:

Objective

Electronic health records (EHR), containing detailed longitudinal clinical information on a large number of patients and covering broad patient populations, open opportunities for comprehensive predictive modeling of disease progression and treatment response. However, since EHRs were originally constructed for administrative purposes not for research, in the EHR-linked studies, it is often not feasible to capture reliable information for analytical variables, especially in the survival setting, when both accurate event status and event times are needed for model building. For example, progression-free survival (PFS), a commonly used survival outcome for cancer patients, often involves complex information embedded in free-text clinical notes and cannot be extracted reliab

SUBMITTER: Hong C 

PROVIDER: S-EPMC10478159 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature