RESUMO
Advances in image quality produced by computed tomography (CT) and the growth in the number of image studies currently performed has made the management of incidental pulmonary nodules (IPNs) a challenging task. This research aims to identify IPNs in radiology reports of chest and abdominal CT by Natural Language Processing techiniques to recognize IPN in sentences of radiology reports. Our preliminary analysis indicates vastly different pulmonary incidental findings rates for two different patient groups.
Assuntos
Sistemas de Apoio a Decisões Clínicas/organização & administração , Diagnóstico por Computador/métodos , Aprendizado de Máquina , Processamento de Linguagem Natural , Radiografia Abdominal/estatística & dados numéricos , Sistemas de Informação em Radiologia/provisão & distribuição , Mineração de Dados/métodos , Humanos , Illinois/epidemiologia , Achados Incidentais , Projetos Piloto , Radiografia Abdominal/classificação , Sistemas de Informação em Radiologia/classificação , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Terminologia como Assunto , Vocabulário ControladoRESUMO
The management of follow-up recommendations is fundamental for the appropriate care of patients with incidental pulmonary findings. The lack of communication of these important findings can result in important actionable information being lost in healthcare provider electronic documents. This study aims to analyze follow-up recommendations in radiology reports containing pulmonary incidental findings by using Natural Language Processing and Regular Expressions. Our evaluation highlights the different follow-up recommendation rates for oncology and non-oncology patient cohorts. The results reveal the need for a context-sensitive approach to tracking different patient cohorts in an enterprise-wide assessment.
