Automated Selection of Relevant Information for Notification of Incident Cancer Cases within a Multisource Cancer Registry

作     者：Jouhet, V. Defossez, G. Ingrand, P. 

作者机构：Ctr Hosp Univ Poitiers Fac Med Unite Epidemiol Biostat & Registre Canc Poitou Ch 6 Rue MilBP 199 F-86034 Poitiers France CHU Bordeaux Serv Informat Med Bordeaux France Univ Poitiers Ctr Regrp Informat & Stat Anat Pathol Fac Med F-86034 Poitiers France Ctr Hosp Gen Georges Renon Coll Reg Informat Med Poitou Charentes Niort France INSERM CIC 802 Poitiers France 

出 版 物：《METHODS OF INFORMATION IN MEDICINE》 (Methods Inf. Med.)

年 卷 期：2013年第52卷第5期

页      面：411-421页

核心收录：

学科分类：1204[管理学-公共管理] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：Multisource information system cancer registry tumour notification selection algorithm relevant information 

摘      要：Objective: The aim of this study was to develop and evaluate a selection algorithm of relevant records for the notification of incident cases of cancer on the basis of the individual data available in a multi-source information system. Methods: This work was conducted on data for the year 2008 in the general cancer registry of Poitou-Charentes region (France). The selection algorithm hierarchizes information according to its level of relevance for tumoral topography and tumoral morphology independently. The selected data are combined to form composite records. These records are then grouped in respect with the notification rules of the International Agency for Research on Cancer for multiple primary cancers. The evaluation, based on recall, precision and F-measure confronted cases validated manually by the registry s physicians with tumours notified with and without records selection. Results: The analysis involved 12,346 tumours validated among 11,971 individuals. The data used were hospital discharge data (104,474 records), pathology data (21,851 records), healthcare insurance data (7508 records) and cancer care centre s data (686 records). The selection algorithm permitted I performances improvement for notification of tumour topography (F-measure 0.926 with vs. 0.857 without selection) and tumour morphology (F-measure 0.805 with vs. 0.750 without selection). Conclusion: These results show that selection of information according to its origin is efficient in reducing noise generated by imprecise coding. Further research is needed for solving the semantic problems relating to the integration of heterogeneous data and the use of non-structured information.