A physiological time series dynamics-based approach to patient monitoring and outcome prediction

Li Wei H. Lehman, Ryan P. Adams, Louis Mayaud, George B. Moody, Atul Malhotra, Roger G. Mark, Shamim Nemati

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether "similar" dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients' health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort.We show that these dynamical behaviors can be used to characterize the physiological "state" of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMICII database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, p = 0.001, p = 0.006 from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone (p = 0.005 and p = 0.045). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.

Original languageEnglish (US)
Article number6846269
Pages (from-to)1068-1076
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Issue number3
StatePublished - May 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Health Information Management
  • Electrical and Electronic Engineering
  • Biotechnology
  • Computer Science Applications


  • Intensive care unit
  • Physiological control systems
  • Switching linear dynamical systems


Dive into the research topics of 'A physiological time series dynamics-based approach to patient monitoring and outcome prediction'. Together they form a unique fingerprint.

Cite this