Exceptional Survival Model Ant-Miner (ESM-AM) is an ant-colony-based Exceptional Model Mining framework to the discovery of subgroups with unusual survival behaviour.
Mattos J.B., Silva E.G., de Mattos Neto P.S.G., Vimieiro R. (2020) Exceptional Survival Model Mining. In: Cerri R., Prati R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science, vol 12320. Springer, Cham. https://doi.org/10.1007/978-3-030-61380-8_21
The development of treatments based on the patient’s individual characteristics has been an emergent medical approach. The objective is to improve individual responses and overall survival. Thus, there is a need for computational tools able to identify and describe subgroups of patients for which the survival response significantly differs from the overall behaviour. However, there are few algorithms that address this matter. The majority of works of literature aim at building predictive models rather than understanding the characteristics that delineate subgroups with unusual survival. The approaches that provide understanding on factors that interfere in the survival behaviour usually resort to the stratification of the data based on previously known variable’s interactions, lacking the ability to shed light into new, possibly unknown, interactions. In contrast to the existent predictive approaches, we propose the use of supervised descriptive pattern mining in order to discover local patterns able to describe subsets of patients that present unusual survival behaviour. In this paper, we present the ESM-AM (Exceptional Survival Model Ant Miner) algorithm, an Exceptional Model Mining approach to the discovery of subgroups with exceptional survival functions that explores the use of ant-colony optimization as search heuristic for the pattern mining task.
The ESM-AM algorithm code configuration used for generating the article's results. More detailed explanation on the usage is provided inside the folder.
The 14 survival databases used in the article's experimental proceadure. The data sets are provided in their original and processed formats, along with information regarding their public domains and sources. The data processing codes are also available.
The complete set of quantitative and qualitative results from the experimental proceadure.