Critical Transitions in Intensive Care Units: A Sepsis Case Study.

ABSTRACT: The progression of complex human diseases is associated with critical transitions across dynamical regimes. These transitions often spawn early-warning signals and provide insights into the underlying disease-driving mechanisms. In this paper, we propose a computational method based on surprise loss (SL) to discover data-driven indicators of such transitions in a multivariate time series dataset of septic shock and non-sepsis patient cohorts (MIMIC-III database). The core idea of SL is to train a mathematical model on time series in an unsupervised fashion and to quantify the deterioration of the model's forecast (out-of-sample) performance relative to its past (in-sample) performance. Considering the highest value of the moving average of SL as a critical transition, our retrospective ana

SUBMITTER: Ghalati PF 

PROVIDER: S-EPMC6733794 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature