Temporal probabilistic profiles for sepsis prediction in the ICU

Eitam Sheetrit, Denis Klimov, Nir Nissim, Yuval Shahar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

32 Scopus citations

Abstract

Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Today, sepsis is one of the leading causes of mortality among populations in intensive care units (ICUs). Sepsis is difficult to predict, diagnose, and treat, as it involves analyzing different sets of multivariate time-series, usually with problems of missing data, different sampling frequencies, and random noise. Here, we propose a new dynamic-behavior-based model, which we call a Temporal Probabilistic proFile (TPF), for classification and prediction tasks of multivariate time series. In the TPF method, the raw, time-stamped data are first abstracted into a series of higher-level, meaningful concepts, which hold over intervals characterizing time periods. We then discover frequently repeating temporal patterns within the data. Using the discovered patterns, we create a probabilistic distribution of the temporal patterns of the overall entity population, of each target class in it, and of each entity. We then exploit TPFs as meta-features to classify the time series of new entities, or to predict their outcome, by measuring their TPF distance, either to the aggregated TPF of each class, or to the individual TPFs of each of the entities, using negative cross entropy. Our experimental results on a large benchmark clinical data set show that TPFs improve sepsis prediction capabilities, and perform better than other machine learning approaches.

Original languageEnglish
Title of host publicationKDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2961-2969
Number of pages9
ISBN (Electronic)9781450362016
DOIs
StatePublished - 25 Jul 2019
Event25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
Duration: 4 Aug 20198 Aug 2019

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Country/TerritoryUnited States
CityAnchorage
Period4/08/198/08/19

Keywords

  • Interval-based temporal patterns
  • Multivariate time-series
  • Sepsis prediction
  • Temporal abstraction

ASJC Scopus subject areas

  • Software
  • Information Systems

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