Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.

ABSTRACT: Rising incidence and mortality of cancer have led to an incremental amount of research in the field. To learn from preexisting data, it has become important to capture maximum information related to disease type, stage, treatment, and outcomes. Medical imaging reports are rich in this kind of information but are only present as free text. The extraction of information from such unstructured text reports is labor-intensive. The use of Natural Language Processing (NLP) tools to extract information from radiology reports can make it less time-consuming as well as more effective. In this study, we have developed and compared different models for the classification of lung carcinoma reports using clinical concepts. This study was approved by the institutional ethics committee as a retrospective

SUBMITTER: Mithun S 

PROVIDER: S-EPMC10287609 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature