J Am Med Inform Assoc - Medical decision support using machine learning for early detection of late-onset neonatal sepsis.

Tópicos

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Resumo

JECTIVE: The objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR).DESIGN: The data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children's Hospital at Vanderbilt and evaluated for late-onset sepsis. Gold standard diagnostic labels (sepsis negative, culture positive sepsis, culture negative/clinical sepsis) were assigned based on all the laboratory, clinical and microbiology data available in EMR. Only data that were available up to 12 h after phlebotomy for blood culture testing were used to build predictive models using machine learning (ML) algorithms.MEASUREMENT: We compared sensitivity, specificity, positive predictive value and negative predictive value of sepsis treatment of physicians with the predictions of models generated by ML algorithms.RESULTS: The treatment sensitivity of all the nine ML algorithms and specificity of eight out of the nine ML algorithms tested exceeded that of the physician when culture-negative sepsis was included. When culture-negative sepsis was excluded both sensitivity and specificity exceeded that of the physician for all the ML algorithms. The top three predictive variables were the hematocrit or packed cell volume, chorioamnionitis and respiratory rate.CONCLUSIONS: Predictive models developed from off-the-shelf and EMR data using ML algorithms exceeded the treatment sensitivity and treatment specificity of clinicians. A prospective study is warranted to assess the clinical utility of the ML algorithms in improving the accuracy of antibiotic use in the management of neonatal sepsis.

Resumo Limpo

jectiv object develop noninvas predict model lateonset neonat sepsi offtheshelf medic data electron medic record emrdesign data use studi infant admit neonat intens care unit monro carel jr children hospit vanderbilt evalu lateonset sepsi gold standard diagnost label sepsi negat cultur posit sepsi cultur negativeclin sepsi assign base laboratori clinic microbiolog data avail emr data avail h phlebotomi blood cultur test use build predict model use machin learn ml algorithmsmeasur compar sensit specif posit predict valu negat predict valu sepsi treatment physician predict model generat ml algorithmsresult treatment sensit nine ml algorithm specif eight nine ml algorithm test exceed physician cultureneg sepsi includ cultureneg sepsi exclud sensit specif exceed physician ml algorithm top three predict variabl hematocrit pack cell volum chorioamnion respiratori rateconclus predict model develop offtheshelf emr data use ml algorithm exceed treatment sensit treatment specif clinician prospect studi warrant assess clinic util ml algorithm improv accuraci antibiot use manag neonat sepsi

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