TY - JOUR TI - A New Approach to Compare the Performance of Two Classification Methods of Causes of Death for Timely Surveillance in France AU - Baghdadi, Yasmine AU - Bourrée, Alix AU - Robert, Aude AU - Rey, Grégoire AU - Gallay, Anne AU - Zweigenbaum, Pierre AU - Grouin, Cyril AU - Fouillet, Anne T2 - Studies in Health Technology and Informatics AB - Timely mortality surveillance in France is based on the monitoring of electronic death certificates to provide information to health authorities. This study aims to analyze the performance of a rule-based and a supervised machine learning method to classify medical causes of death into 60 mortality syndromic groups (MSGs). Performance was first measured on a test set. Then we compared the trends of the monthly numbers of deaths classified into MSGs from 2012 to 2016 using both methods. Among the 60 MSGs, 31 achieved recall and precision over 0.95 for either one or the other method on the test set. On the whole dataset, the correlation coefficient of the monthly numbers of deaths obtained by the two methods were close to 1 for 21 of the 31 MSGs. This approach is useful for analyzing a large number of categories or when annotated resources are limited. DA - 2019/08/21/ PY - 2019 DO - 10.3233/SHTI190359 DP - PubMed VL - 264 SP - 925 EP - 929 J2 - Stud Health Technol Inform LA - eng SN - 1879-8365 KW - Cause of Death KW - Death Certificates KW - France KW - Health Resources KW - Humans KW - Machine learning KW - Supervised Machine Learning KW - cause of death KW - sentinel surveillance ER -