Evaluating Different Selection Criteria for Phase Type Survival Tree Construction

Lalit Garg, Sally I. McClean, Maria Barton, Brian J. Meenan, Ken Fullerton, Georgios Kontonatsios, Marcello Trovati*, Ioannis Konkontzelos, Xiaolong Xu, Mohsen Farid

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

2 Citations (Scopus)
25 Downloads (Pure)


Due to its interpretability and intuitiveness, survival tree based analysis is a powerful Artificial Intelligence method for modelling longitudinal survival data, its relationship with covariates and the interrelationship between covariates. Furthermore, it is being increasingly used for a range of applications including clustering, prognostication and classification. Phase type survival tree methods have been demonstrated to have important applications, including clustering patients into clinically meaningful groups, patient pathway prognostication and forecasting bed requirements. In this article, we critically investigate and assess several selection information criteria with regards to their suitability and limitations when used as splitting criteria in phase type survival tree construction. As shown in Table 12, the results of this analysis are compared and discussed. Furthermore, a text mining approach is utilised to further assess correlations, which have been extracted from hospital data, between the three underlying diseases and the two different types of population groups, namely age and gender groups. Its aim is to provide further investigative tools. In fact, due to its ability to analyse large volumes of textual data, text mining can provide a useful approach to this research area.

Original languageEnglish
Article number100250
JournalBig Data Research
Early online date19 Aug 2021
Publication statusE-pub ahead of print - 19 Aug 2021


  • Model selection information criteria
  • Phase type survival tree
  • Text mining


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