Accepted Papers

Temporal Probabilistic Profiles for Sepsis Prediction in the ICU

Eitam Sheetrit, Nir Nissim, Denis Klimov and Yuval Shahar


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.

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