Utilizing Longitudinal Chest X-Rays and Reports to Pre-fill Radiology Reports.

ABSTRACT: Despite the reduction in turn-around times in radiology reporting with the use of speech recognition software, persistent communication errors can significantly impact the interpretation of radiology reports. Pre-filling a radiology report holds promise in mitigating reporting errors, and despite multiple efforts in literature to generate comprehensive medical reports, there lacks approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset. To address this gap, we propose to use longitudinal multi-modal data, i.e., previous patient visit CXR, current visit CXR, and the previous visit report, to pre-fill the "findings" section of the patient's current visit. We first gathered the longitudinal visit information for 26,625 patients from the MIMIC-CXR data

SUBMITTER: Zhu Q 

PROVIDER: S-EPMC10947431 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature