title: Classification of cancer-related death certificates using machine learning.
abstract: BACKGROUND
  Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities.
  
  
  OBJECTIVE
  In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated.
  
  
  METHODS
  A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes.
  
  
  RESULTS
  Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall Fmeasure of 0.9866 when evaluated on a set of 5,000 freetext death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers.
  
  
  CONCLUSIONS
  The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.
authors.affiliation: The Australian e-Health Research Centre, Brisbane, Queensland, Australia;
authors.forename: Luke
authors.initials: L
authors.lastname: Butt
authors.forename: Guido
authors.initials: G
authors.lastname: Zuccon
authors.forename: Anthony
authors.initials: A
authors.lastname: Nguyen
authors.forename: Anton
authors.initials: A
authors.lastname: Bergheim
authors.forename: Narelle
authors.initials: N
authors.lastname: Grayson
keywords: Cancer Registry; Death certificates; SNOMED CT; cancer monitoring and reporting; machine learning; natural language processing