Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making

Seale AC, Blencowe H, Manu AA, Nair H, Bahl R, Qazi SA, et al. Estimates of possible severe bacterial infection in neonates in sub-Saharan Africa, south Asia, and Latin America for 2012: a systematic review and meta-analysis. Lancet Infect Dis. 2014;14(8):731–41.

Article  PubMed  PubMed Central  Google Scholar 

Polin RA. Management of neonates with suspected or proven early-onset bacterial sepsis. Pediatrics. 2012;129(5):1006–15.

Article  PubMed  Google Scholar 

Rallis D, Giapros V, Serbis A, Kosmeri C, Baltogianni M. Fighting antimicrobial resistance in neonatal intensive care units: rational use of antibiotics in neonatal sepsis. Antibiotics (Basel). 2023;12(3):508.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Perin J, Mulick A, Yeung D, Villavicencio F, Lopez G, Strong KL, et al. Global, regional, and national causes of under-5 mortality in 2000–19: an updated systematic analysis with implications for the Sustainable Development Goals. Lancet Child Adolesc Health. 2022;6(2):106–15.

Article  PubMed  PubMed Central  Google Scholar 

Molyneux E, Gest A. Neonatal sepsis: an old issue needing new answers. Lancet Infect Dis. 2015;15(5):503–5.

Article  PubMed  Google Scholar 

Stocker M, Klingenberg C, Naver L, Nordberg V, Berardi A, El Helou S, et al. Less is more: antibiotics at the beginning of life. Nat Commun. 2023;14(1):2423.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chaurasia S, Sivanandan S, Agarwal R, Ellis S, Sharland M, Sankar MJ. Neonatal sepsis in South Asia: huge burden and spiralling antimicrobial resistance. BMJ. 2019;22(364): k5314.

Article  Google Scholar 

Esaiassen E, Fjalstad JW, Juvet LK, van den Anker JN, Klingenberg C. Antibiotic exposure in neonates and early adverse outcomes: a systematic review and meta-analysis. J Antimicrob Chemother. 2017;72(7):1858–70.

Article  CAS  PubMed  Google Scholar 

Reyman M, van Houten MA, Watson RL, Chu M, Arp K, de Waal WJ, et al. Effects of early-life antibiotics on the developing infant gut microbiome and resistome: a randomized trial. Nat Commun. 2022;13(1):893.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Samuel AL. Some studies in machine learning using the game of checkers. Ibm J Res Dev. 1959;3(3):211–20.

Article  Google Scholar 

Fleuren LM, Klausch TLT, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 2020;46(3):383–400.

Article  PubMed  PubMed Central  Google Scholar 

Islam MM, Nasrin T, Walther BA, Wu CC, Yang HC, Li YC. Prediction of sepsis patients using machine learning approach: a meta-analysis. Comput Methods Programs Biomed. 2019;170:1–9.

Article  PubMed  Google Scholar 

Marques L, Costa B, Pereira M, Silva A, Santos J, Saldanha L, et al. Advancing precision medicine: a review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics. 2024;16(3):332.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ribba B, Dudal S, Lave T, Peck RW. Model-informed artificial intelligence: reinforcement learning for precision dosing. Clin Pharmacol Ther. 2020;107(4):853–7.

Article  PubMed  Google Scholar 

Stankeviciute K, Woillard JB, Peck RW, Marquet P, van der Schaar M. Bridging the worlds of pharmacometrics and machine learning. Clin Pharmacokinet. 2023;62(11):1551–65.

Article  PubMed  Google Scholar 

Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36–40.

Article  PubMed  Google Scholar 

Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40–55.

Article  CAS  PubMed  Google Scholar 

Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.

Article  PubMed  PubMed Central  Google Scholar 

Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–19.

Article  CAS  PubMed  Google Scholar 

Rawson TM, Wilson RC, O’Hare D, Herrero P, Kambugu A, Lamorde M, et al. Optimizing antimicrobial use: challenges, advances and opportunities. Nat Rev Microbiol. 2021;19(12):747–58.

Article  CAS  PubMed  Google Scholar 

Hentges CR, Silveira RC, Procianoy RS, Carvalho CG, Filipouski GR, Fuentefria RN, et al. Association of late-onset neonatal sepsis with late neurodevelopment in the first two years of life of preterm infants with very low birth weight. J Pediatr (Rio J). 2014;90(1):50–7.

Article  PubMed  Google Scholar 

Mukhopadhyay S, Puopolo KM, Hansen NI, Lorch SA, DeMauro SB, Greenberg RG, et al. Neurodevelopmental outcomes following neonatal late-onset sepsis and blood culture-negative conditions. Arch Dis Child Fetal Neonatal Ed. 2021;106(5):467–73.

Article  PubMed  Google Scholar 

Han M, Fitzgerald JC, Balamuth F, Keele L, Alpern ER, Lavelle J, et al. Association of delayed antimicrobial therapy with one-year mortality in pediatric sepsis. Shock. 2017;48(1):29–35.

Article  PubMed  PubMed Central  Google Scholar 

Sankar J, Garg M, Ghimire JJ, Sankar MJ, Lodha R, Kabra SK. Delayed administration of antibiotics beyond the first hour of recognition is associated with increased mortality rates in children with sepsis/severe sepsis and septic shock. J Pediatr. 2021;233(183–90): e3.

Google Scholar 

Weiss SL, Fitzgerald JC, Balamuth F, Alpern ER, Lavelle J, Chilutti M, et al. Delayed antimicrobial therapy increases mortality and organ dysfunction duration in pediatric sepsis. Crit Care Med. 2014;42(11):2409–17.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Celik IH, Hanna M, Canpolat FE, Mohan P. Diagnosis of neonatal sepsis: the past, present and future. Pediatr Res. 2022;91(2):337–50.

Article  PubMed  Google Scholar 

Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R, et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc. 2014;21(2):326–36.

Article  PubMed  Google Scholar 

Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS ONE. 2019;14(2): e0212665.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Leon C, Carrault G, Pladys P, Beuchee A. Early detection of late onset sepsis in premature infants using visibility graph analysis of heart rate variability. IEEE J Biomed Health Inform. 2021;25(4):1006–17.

Article  PubMed  Google Scholar 

Stocker M, Daunhawer I, van Herk W, El Helou S, Dutta S, Schuerman F, et al. Machine learning used to compare the diagnostic accuracy of risk factors, clinical signs and biomarkers and to develop a new prediction model for neonatal early-onset sepsis. Pediatr Infect Dis J. 2022;41(3):248–54.

Article  PubMed  Google Scholar 

Ramgopal S, Horvat CM, Yanamala N, Alpern ER. Machine learning to predict serious bacterial infections in young febrile infants. Pediatrics. 2020;146(3): e20194096.

Article  PubMed  Google Scholar 

Sahu P, Raj Stanly EA, Simon Lewis LE, Prabhu K, Rao M, Kunhikatta V. Prediction modelling in the early detection of neonatal sepsis. World J Pediatr. 2022;18(3):160–75.

Article  PubMed  PubMed Central 

Comments (0)

No login
gif