Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection

Robert Moskovitch, Hyunmi Choi, George Hripcsak, Nicholas Tatonetti

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Accurate prognosis of outcome events, such as clinical procedures or disease diagnosis, is central in medicine. The emergence of longitudinal clinical data, like the Electronic Health Records (EHR), represents an opportunity to develop automated methods for predicting patient outcomes. However, these data are highly dimensional and very sparse, complicating the application of predictive modeling techniques. Further, their temporal nature is not fully exploited by current methods, and temporal abstraction was recently used which results in symbolic time intervals representation. We present Maitreya, a framework for the prediction of outcome events that leverages these symbolic time intervals. Using Maitreya, learn predictive models based on the temporal patterns in the clinical records that are prognostic markers and use these markers to train predictive models for eight clinical procedures. In order to decrease the number of patterns that are used as features, we propose the use of three one class feature selection methods. We evaluate the performance of Maitreya under several parameter settings, including the one-class feature selection, and compare our results to that of atemporal approaches. In general, we found that the use of temporal patterns outperformed the atemporal methods, when representing the number of pattern occurrences.

Original languageEnglish
Article number7513445
Pages (from-to)555-563
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number3
StatePublished - 1 May 2017
Externally publishedYes


  • Time intervals mining
  • prediction
  • temporal patterns

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


Dive into the research topics of 'Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection'. Together they form a unique fingerprint.

Cite this