Constraint based temporal event sequence mining for Glioblastoma survival prediction

Kunal Malhotra, Shamkant B. Navathe, Duen Horng Chau, Costas Hadjipanayis, Jimeng Sun

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Objective: A significant challenge in treating rare forms of cancer such as Glioblastoma (GBM) is to find optimal personalized treatment plans for patients. The goals of our study is to predict which patients survive longer than the median survival time for GBM based on clinical and genomic factors, and to assess the predictive power of treatment patterns. Method: We developed a predictive model based on the clinical and genomic data from approximately 300 newly diagnosed GBM patients for a period of 2 years. We proposed sequential mining algorithms with novel clinical constraints, namely, 'exact-order' and 'temporal overlap' constraints, to extract treatment patterns as features used in predictive modeling. With diverse features from clinical, genomic information and treatment patterns, we applied both logistic regression model and Cox regression to model patient survival outcome. Results: The most predictive features influencing the survival period of GBM patients included mRNA expression levels of certain genes, some clinical characteristics such as age, Karnofsky performance score, and therapeutic agents prescribed in treatment patterns. Our models achieved c-statistic of 0.85 for logistic regression and 0.84 for Cox regression. Conclusions: We demonstrated the importance of diverse sources of features in predicting GBM patient survival outcome. The predictive model presented in this study is a preliminary step in a long-term plan of developing personalized treatment plans for GBM patients that can later be extended to other types of cancers.

Original languageEnglish
Pages (from-to)267-275
Number of pages9
JournalJournal of Biomedical Informatics
StatePublished - 1 Jun 2016
Externally publishedYes


  • Classification
  • Glioblastoma
  • Graph mining
  • Predictive model
  • Sequential pattern mining
  • Treatment patterns


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