Data mining technique for identifying mortality patterns in a pulmonary tuberculosis cohort.

Tuberculosis (TB) deaths occur early, mainly during the two months comprising the intensive treatment phase compared to other causes of death. Understanding the distinct TB mortality patterns provides more effective health care for specific groups. This study aimed to apply a data mining technique to examine whether there are distinct mortality patterns associated with diverse patient profiles within a cohort notified with TB in the city of Rio de Janeiro, Brazil. This was a retrospective cohort study using a probabilistic linkage between the Brazilian Information System for Notifiable Diseases (SINAN) and the Brazilian Mortality Information System (SIM). An association rule data mining technique was applied using the Apriori algorithm to evaluate patterns associated with causes of death. TB deaths were associated with adult men with short survival time. Deaths from AIDS with TB occurred during the treatment period and were more associated with women. In contrast, deaths from AIDS without a mention of TB were more frequent among younger men and Black/Mixed-race adults. Deaths from external causes occurred later, one year after diagnosis, and were related to young men. Deaths from other causes that were linked to longer survivals were associated with older white individuals with higher education. The Apriori algorithm provided the extraction of relevant knowledge from secondary data, such as identifying distinct subgroups of people who can benefit from specific actions, with a consequent positive effect for TB control.