J Am Med Inform Assoc - From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system.


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JECTIVE: 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 (na?ve 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.

Resumo Limpo

jectiv develop decis support system identifi patient high risk hyperlactatemia base upon routin measur vital sign laboratori studiesmateri method electron health record adult patient univers california davi health system met least two system inflammatori respons syndrom criteria use associ patient vital sign white blood cell count wbc sepsi occurr mortal generat discrimin classif nave bay support vector machin gaussian mixtur model hidden markov model use integr heterogen patient data form predict tool infer lactat level mortal riskresult accuraci discrimin area receiv oper characterist curv auc lactat level predict obtain vital sign wbc measur analys h time bin accuraci discrimin auc mortal predict patient sepsi achiev three featur median lactat level mean arteri pressur median absolut deviat respiratori ratediscuss studi introduc new scheme predict lactat level mortal risk patient vital sign wbc accur predict variabl can drive appropri respons clinic staff thus may import implic patient health treatment outcomeconclus effect predict lactat level mortal risk can provid clinic variabl tempor aspect variabl patient data consid

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