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Vital sign-based detection of sepsis in neonates using machine learning
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden.ORCID iD: 0000-0003-0166-1356
Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden.
Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
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2023 (English)In: Acta Paediatrica, ISSN 0803-5253, E-ISSN 1651-2227, Vol. 112, no 4, p. 686-696Article in journal (Refereed) Published
Abstract [en]

Aim: Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non-specific signs. We investigate the predictive value of machine learning-assisted analysis of non-invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis. Methods: Single centre study, including a representative cohort of 325 infants (2866 hospitalisation days). Personalised event timelines including interventions and clinical findings were generated. Time-domain features from heart rate, respiratory rate and oxygen saturation values were calculated and demographic factors included. Sepsis prediction was performed using Naïve Bayes algorithm in a maximum a posteriori framework up to 24 h before clinical sepsis suspicion. Results: Twenty sepsis cases were identified. Combining multiple vital signs improved algorithm performance compared to heart rate characteristics alone. This enabled a prediction of sepsis with an area under the receiver operating characteristics curve of 0.82, up to 24 h before clinical sepsis suspicion. Moreover, 10 h prior to clinical suspicion, the risk of sepsis increased 150-fold. Conclusion: The present algorithm using non-invasive patient data provides useful predictive value for neonatal sepsis detection. Machine learning-assisted algorithms are promising novel methods that could help individualise patient care and reduce morbidity and mortality.

Place, publisher, year, edition, pages
Wiley , 2023. Vol. 112, no 4, p. 686-696
Keywords [en]
artificial intelligence, clinical decision support system, Naïve Bayes classifier, physiological monitoring, prediction, respiration-related
National Category
Pediatrics Medical and Health Sciences
Identifiers
URN: urn:nbn:se:kth:diva-330037DOI: 10.1111/apa.16660ISI: 000919031600001PubMedID: 36607251Scopus ID: 2-s2.0-85147263614OAI: oai:DiVA.org:kth-330037DiVA, id: diva2:1775689
Note

QC 20230627

Available from: 2023-06-27 Created: 2023-06-27 Last updated: 2023-06-27Bibliographically approved

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Honoré, AntoineChatterjee, Saikat

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