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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 315-325 |
| Number of pages | 11 |
| Journal | Journal of the American Medical Informatics Association |
| Volume | 21 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2014 |
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ASJC Scopus subject areas
- Health Informatics
Cite this
From vital signs to clinical outcomes for patients with sepsis : A machine learning basis for a clinical decision support system. / Gultepe, Eren; Green, Jeffrey P.; Nguyen, Hien H; Adams, Jason Yeates; Albertson, Timothy E; Tagkopoulos, Ilias.
In: Journal of the American Medical Informatics Association, Vol. 21, No. 2, 2014, p. 315-325.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - From vital signs to clinical outcomes for patients with sepsis
T2 - A machine learning basis for a clinical decision support system
AU - Gultepe, Eren
AU - Green, Jeffrey P.
AU - Nguyen, Hien H
AU - Adams, Jason Yeates
AU - Albertson, Timothy E
AU - Tagkopoulos, Ilias
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84894101089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894101089&partnerID=8YFLogxK
U2 - 10.1136/amiajnl-2013-001815
DO - 10.1136/amiajnl-2013-001815
M3 - Article
VL - 21
SP - 315
EP - 325
JO - Journal of the American Medical Informatics Association : JAMIA
JF - Journal of the American Medical Informatics Association : JAMIA
SN - 1067-5027
IS - 2
ER -