Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
J Am Med Inform Assoc. 2014 Mar-Apr;21(2):315-25. doi: 10.1136/amiajnl-2013-001815. Epub 2013 Aug 19.

From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system.

Author information

  • 1Department of Biomedical Engineering, University of California, Davis, California, USA.

Abstract

OBJECTIVE:

To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies.

MATERIALS AND METHODS:

Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients' vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk.

RESULTS:

An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate.

DISCUSSION:

This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome.

CONCLUSIONS:

Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered.

KEYWORDS:

Clinical Decision Support; Electronic Health Records; Lactate Level Prediction; Machine Learning; Mortality Prediction; Sepsis

PMID:
23959843
[PubMed - indexed for MEDLINE]
PMCID:
PMC3932455
[Available on 2015/3/1]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Loading ...
    Write to the Help Desk