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dc.contributor.authorKamaleswaran, Rishikesan
dc.contributor.authorSataphaty, Sanjaya K
dc.contributor.authorMas, Valeria R
dc.contributor.authorEason, James D
dc.contributor.authorMaluf, Daniel G
dc.date.accessioned2021-09-30T13:55:22Z
dc.date.available2021-09-30T13:55:22Z
dc.date.issued2021-09-06
dc.identifier.urihttp://hdl.handle.net/10713/16745
dc.description.abstractBackground: Sepsis, post-liver transplantation, is a frequent challenge that impacts patient outcomes. We aimed to develop an artificial intelligence method to predict the onset of post-operative sepsis earlier. Methods: This pilot study aimed to identify "physiomarkers" in continuous minute-by-minute physiologic data streams, such as heart rate, respiratory rate, oxygen saturation (SpO2), and blood pressure, to predict the onset of sepsis. The model was derived from a cohort of 5,748 transplant and non-transplant patients across intensive care units (ICUs) over 36 months, with 92 post-liver transplant patients who developed sepsis. Results: Using an alert timestamp generated with the Third International Consensus Definition of Sepsis (Sepsis-3) definition as a reference point, we studied up to 24 h of continuous physiologic data prior to the event, totaling to 8.35 million data points. One hundred fifty-five features were generated using signal processing and statistical methods. Feature selection identified 52 highly ranked features, many of which included blood pressures. An eXtreme Gradient Boost (XGB) classifier was then trained on the ranked features by 5-fold cross validation on all patients (n = 5,748). We identified that the average sensitivity, specificity, positive predictive value (PPV), and area under the receiver-operator curve (AUC) of the model after 100 iterations was 0.94 ± 0.02, 0.9 ± 0.02, 0.89 ± 0.01, respectively, and 0.97 ± 0.01 for predicting sepsis 12 h before meeting criteria. Conclusion: The data suggest that machine learning/deep learning can be applied to continuous streaming data in the transplant ICU to monitor patients and possibly predict sepsis.en_US
dc.description.urihttps://doi.org/10.3389/fphys.2021.692667en_US
dc.language.isoenen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofFrontiers in Physiologyen_US
dc.rightsCopyright © 2021 Kamaleswaran, Sataphaty, Mas, Eason and Maluf.en_US
dc.subjectartificial intelligenceen_US
dc.subjectliver transplanten_US
dc.subjectmachine learningen_US
dc.subjectphysiological data streamsen_US
dc.subjectsepsisen_US
dc.subjectsurgeryen_US
dc.titleArtificial Intelligence May Predict Early Sepsis After Liver Transplantationen_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fphys.2021.692667
dc.identifier.pmid34552499
dc.source.volume12
dc.source.beginpage692667
dc.source.endpage
dc.source.countrySwitzerland


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