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Exceptional Survival Model Mining

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Intelligent Systems (BRACIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12320))

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Abstract

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.

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Notes

  1. 1.

    LR-Rules algorithm: https://github.com/adaa-polsl/LR-Rules/releases.

  2. 2.

    ESM-AM algorithm website: https://github.com/jbmattos/ESM-AM_bracis2020.

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Acknowledgment

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and by the National Council for Scientific and Technological Development – CNPq.

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Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil

    Juliana Barcellos Mattos, Eraylson G. Silva & Paulo S. G. de Mattos Neto

  2. Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Renato Vimieiro

Authors
  1. Juliana Barcellos Mattos
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  2. Eraylson G. Silva
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  3. Paulo S. G. de Mattos Neto
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  4. Renato Vimieiro
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Corresponding author

Correspondence to Juliana Barcellos Mattos .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Ricardo Cerri

  2. Federal University of ABC, Santo Andre, Brazil

    Ronaldo C. Prati

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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

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  • DOI: https://doi.org/10.1007/978-3-030-61380-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61379-2

  • Online ISBN: 978-3-030-61380-8

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