The use of machine learning and artificial intelligence within pediatric critical care

Jung, M. et al. Age-specific distribution of diagnosis and outcomes of children admitted to ICUs: a population-based cohort study. Pediatr. Crit. Care Med. 20, e301–e310 (2019).

PubMed  Google Scholar 

Crow, S. S. et al. Epidemiology of pediatric critical illness in a population-based birth cohort in Olmsted County, MN. Pediatr. Crit. Care Med. 18, e137–e145 (2017).

PubMed  PubMed Central  Google Scholar 

Epstein, D. & Brill, J. E. A history of pediatric critical care medicine. Pediatr. Res. 58, 987–996 (2005).

PubMed  Google Scholar 

Gupta, P., Gossett, J. & Rao Rettiganti, M. 60: Trends in mortality rates in pediatric intensive care units in the United States from 2004 to 2015. Crit. Care Med. 46, 30 (2018).

Google Scholar 

Markovitz, B. P., Kukuyeva, I., Soto-Campos, G. & Khemani, R. G. PICU volume and outcome: a severity-adjusted analysis. Pediatr. Crit. Care Med. 17, 483–489 (2016).

PubMed  PubMed Central  Google Scholar 

Weiss, S. L. et al. Surviving sepsis campaign international guidelines for the management of septic shock and sepsis-associated organ dysfunction in children. Pediatr. Crit. Care Med. 21, e52–e106 (2020).

PubMed  Google Scholar 

Kochanek, P. M. et al. Management of pediatric severe traumatic brain injury: 2019 consensus and guidelines-based algorithm for first and second tier therapies. Pediatr. Crit. Care Med. 20, 269–279 (2019).

PubMed  Google Scholar 

Helm, J. M. et al. Machine learning and artificial intelligence: definitions, applications, and future directions. Curr. Rev. Musculoskelet. Med. 13, 69–76 (2020).

PubMed  PubMed Central  Google Scholar 

Gutierrez, G. Artificial intelligence in the intensive care unit. Crit. Care 24, 101 (2020).

PubMed  PubMed Central  Google Scholar 

Lovejoy, C. A., Buch, V. & Maruthappu, M. Artificial intelligence in the intensive care unit. Crit. Care 23, 7 (2019).

PubMed  PubMed Central  Google Scholar 

Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).

PubMed  Google Scholar 

Sanchez-Pinto, L. N., Luo, Y. & Churpek, M. M. Big data and data science in critical care. Chest 154, 1239–1248 (2018).

PubMed  PubMed Central  Google Scholar 

Williams, J. B., Ghosh, D. & Wetzel, R. C. Applying machine learning to pediatric critical care data. Pediatr. Crit. Care Med. 19, 599–608 (2018).

PubMed  Google Scholar 

Alanazi, H. O., Abdullah, A. H. & Qureshi, K. N. A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J. Med. Syst. 41, 69 (2017).

PubMed  Google Scholar 

Lonsdale, H., Jalali, A., Ahumada, L. & Matava, C. Machine learning and artificial intelligence in pediatric research: current state, future prospects, and examples in perioperative and critical care. J. Pediatr. 221S, S3–S10 (2020).

PubMed  Google Scholar 

Choudhary, R. & Gianey, H. K. Comprehensive review on supervised machine learning algorithms. In 2017 International Conference on Machine Learning and Data Science (MLDS) 37–43 (2017).

Shafaf, N. & Malek, H. Applications of machine learning approaches in emergency medicine; a review article. Arch. Acad. Emerg. Med. 7, 34 (2019).

PubMed  PubMed Central  Google Scholar 

Chowdhury, A., Rosenthal, J., Waring, J. & Umeton, R. Applying self-supervised learning to medicine: review of the state of the art and medical implementations. Informatics 8, 59 (2021).

Google Scholar 

Grogan, K. L. et al. A narrative review of analytics in pediatric cardiac anesthesia and critical care medicine. J. Cardiothorac. Vasc. Anesth. 34, 479–482 (2020).

PubMed  Google Scholar 

Hajian-Tilaki, K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Casp. J. Intern. Med. 4, 627–635 (2013).

Google Scholar 

Sidey-Gibbons, J. A. M. & Sidey-Gibbons, C. J. Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19, 64 (2019).

PubMed  PubMed Central  Google Scholar 

Zhai, Q. et al. Using machine learning tools to predict outcomes for emergency department intensive care unit patients. Sci. Rep. 10, 20919 (2020).

CAS  PubMed  PubMed Central  Google Scholar 

Wong, H. R. et al. Combining prognostic and predictive enrichment strategies to identify children with septic shock responsive to corticosteroids. Crit. Care Med. 44, e1000–e1003 (2016).

CAS  PubMed  PubMed Central  Google Scholar 

Ramgopal, S., Horvat, C. M., Yanamala, N. & Alpern, E. R. Machine learning to predict serious bacterial infections in young febrile infants. Pediatrics https://doi.org/10.1542/peds.2019-4096 (2020).

Berger, R. P. et al. Derivation and validation of a serum biomarker panel to identify infants with acute intracranial hemorrhage. JAMA Pediatr. 171, e170429 (2017).

PubMed  Google Scholar 

Kothalawala, D. M. et al. Prediction models for childhood asthma: a systematic review. Pediatr. Allergy Immunol. 31, 616–627 (2020).

PubMed  Google Scholar 

Kwon, J. M. et al. Deep learning algorithm to predict need for critical care in pediatric emergency departments. Pediatr. Emerg. Care 37, e988–e994 (2021).

PubMed  Google Scholar 

Rusin, C. G. et al. Prediction of imminent, severe deterioration of children with parallel circulations using real-time processing of physiologic data. J. Thorac. Cardiovasc. Surg. 152, 171–177 (2016).

PubMed  PubMed Central  Google Scholar 

Park, S. J. et al. Development and validation of a deep-learning-based pediatric early warning system: a single-center study. Biomed. J. https://doi.org/10.1016/j.bj.2021.01.003 (2021).

Zhai, H. et al. Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children. Resuscitation 85, 1065–1071 (2014).

PubMed  PubMed Central  Google Scholar 

Chen, B. et al. Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning. J. Am. Med. Inf. Assoc. 28, 1168–1177 (2021).

Google Scholar 

Reddy, K. et al. Subphenotypes in critical care: translation into clinical practice. Lancet Respir. Med. 8, 631–643 (2020).

PubMed  Google Scholar 

Dahmer, M. K. et al. Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis. Lancet Respir. Med. https://doi.org/10.1016/S2213-2600(21)00382-9 (2021).

Zhang, Z., Zhang, G., Goyal, H., Mo, L. & Hong, Y. Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis. Crit. Care 22, 347 (2018).

PubMed  PubMed Central  Google Scholar 

Kolli, S. et al. 973: latent class analysis of pediatric patients with near-fatal asthma. Crit. Care Med. 49, 484 (2021).

Google Scholar 

Sinha, P., Calfee, C. S. & Delucchi, K. L. Practitioner’s guide to latent class analysis: methodological considerations and common pitfalls. Crit. Care Med. 49, e63–e79 (2021).

PubMed  PubMed Central  Google Scholar 

Calfee, C. S. et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir. Med. 6, 691–698 (2018).

CAS  PubMed  PubMed Central  Google Scholar 

Famous, K. R. et al. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. Am. J. Respir. Crit. Care Med. 195, 331–338 (2017).

CAS  PubMed  PubMed Central  Google Scholar 

Calfee, C. S. et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir. Med. 2, 611–620 (2014).

PubMed  PubMed Central  Google Scholar 

A, F., Shah, N., Z, W. & Raman, L. Machine learning: Brief overview for biomedical researchers. J. Transl. Sci. https://doi.org/10.15761/JTS.1000343 (2020).

Meyer, A. et al. Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir. Med. 6, 905–914 (2018).

PubMed  Google Scholar 

Kamaleswaran, R. et al. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU. Pediatr. Crit. Care Med. 19, e495–e503 (2018).

PubMed  Google Scholar 

Shah, N. et al. Neural networks to predict radiographic brain injury in pediatric patients treated with extracorporeal membrane oxygenation. J. Clin. Med. https://doi.org/10.3390/jcm9092718 (2020).

DeGrave, A. J., Janizek, J. D. & Lee, S. I. AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3, 610–619 (2021).

Savage, N. Breaking into the black box of artificial intelligence. Nature https://doi.org/10.1038/d41586-022-00858-1 (2022).

Yeh, T. S., Pollack, M. M., Ruttimann, U. E., Holbrook, P. R. & Fields, A. I. Validation of a physiologic stability index for use in critically ill infants and children. Pediatr. Res. 18, 445–451 (1984).

CAS  PubMed  Google Scholar 

Pollack, M. M., Ruttimann, U. E. & Getson, P. R. Pediatric risk of mortality (PRISM) score. Crit. Care Med. 16, 1110–1116 (1988).

CAS  PubMed  Google Scholar 

Shann, F., Pearson, G., Slater, A. & Wilkinson, K. Paediatric index of mortality (PIM): a mortality prediction model for children in intensive care. Intensive Care Med. 23, 201–207 (1997).

CAS  PubMed  Google Scholar 

Straney, L. et al. Paediatric index of mortality 3: an updated model for predicting mortality in pediatric intensive care. Pediatr. Crit. Care Med. 14, 673–681 (2013).

PubMed  Google Scholar 

Pollack, M. M. et al. The Pediatric Risk of Mortality Score: update 2015. Pediatr. Crit. Care Med. 17, 2–9 (2016).

PubMed  PubMed Central  Google Scholar 

Bembea, M. M. et al. Pediatric Organ Dysfunction Information Update Mandate (PODIUM) contemporary organ dysfunction criteria: executive summary. Pediatrics 149, S1–S12 (2022).

PubMed  Google Scholar 

Spaeder, M. C. et al. Predictive analytics in the pediatric intensive care unit for early identification of sepsis: capturing the context of age. Pediatr. Res. 86, 655–661 (2019).

PubMed  Google Scholar 

Liu, R. et al. Prediction of impending septic shock in children with sepsis. Crit. Care Explor 3, e0442 (2021).

PubMed  PubMed Central  Google Scholar 

Scott, H. F. et al. Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival. J. Pediatr. 217, 145.e6–151.e6 (2020).

Google Scholar 

Zhou, H., Albrecht, M. A., Roberts, P. A., Porter, P. & Della, P. R. Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation. Aust. Health Rev. 45, 328–337 (2021).

PubMed 

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