Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Oleynik, M; Patrão, DFC; Finger, M.
Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.
Stud Health Technol Inform. 2017; 235(11): 256-260.
PubMed
- Führende Autor*innen der Med Uni Graz
- Oleynik Michel
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- Abstract:
- Pathology reports are a main source of information regarding cancer diagnosis and are commonly written following semi-structured templates that include tumour localisation and behaviour. In this work, we evaluated the efficiency of support vector machines (SVMs) to classify pathology reports written in Portuguese into the International Classification of Diseases for Oncology (ICD-O), a biaxial classification of cancer topography and morphology. A partnership program with the Brazilian hospital A.C. Camargo Cancer Center provided anonymised pathology reports and structured data from 94,980 patients used for training and validation. We employed SVMs with tf-idf weighting scheme in a bag-of-words approach and report F1 score of 0.82 for 18 sites and 0.73 for 49 morphology classes. With the largest dataset ever used in such a task, our work provides reliable estimates for the classification of pathology reports in Portuguese and agrees with a few similar studies published in the same kind of data in other languages.
- Find related publications in this database (using NLM MeSH Indexing)
- Brazil -
- Humans -
- International Classification of Diseases - organization & administration
- Neoplasms - diagnosis
- Neoplasms - pathology
- Registries -
- Support Vector Machine -