Automated Classification of Pathology Reports.
This work develops an automated classifier of pathology reports which infers the topography and the morphology classes of a tumor using codes from the International Classification of Diseases for Oncology (ICD-O). Data from 94,980 patients of the A.C. Camargo Cancer Center was used for training and validation of Naive Bayes classifiers, evaluated by the F1-score. Measures greater than 74% in the topographic group and 61% in the morphologic group are reported. Our work provides a successful baseline for future research for the classification of medical documents written in Portuguese and in other domains.
Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
Michel
M
Oleynik
Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.
Marcelo
M
Finger
International Center for Research, A. C. Camargo Cancer Center, São Paulo, Brazil.
Diogo F C
DF
Patrão
D001938:Brazil / Q000453:epidemiology; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D038801:International Classification of Diseases; D009323:Natural Language Processing; D009369:Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000473:pathology*; D010336:Pathology / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods; D015995:Prevalence; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity