Background: The functionality of Artificial intelligence (AI) in paediatric practices has been gaining more attention for last five years. Since then, researchers have started observing that the techniques are helpful in dealing multiple facets of childhood diseases including emergency like situations. This article has been aimed to discuss the current status of usefulness of AI in paediatric emergencies. Methods: Total 22 research articles have been reviewed. Articles were searched from electronic database like Pubmed, Medline, Google scholar. Artificial intelligence, machine learning, paediatric emergencies, childhood diseases were the key words used to ease the search. Results: Out of 22, 15 were chosen as relatable to paediatric emergency situations per se. After reviewing the available literature, the utility of AI in paediatric emergencies had been discussed under four sub headings: i) Diagnosis; ii) Predictive modelling iii) Assistance in Antimicrobial stewardship iv) Management of emergency department resources. Conclusion: AI and different machine learning techniques have been proven as reliable accompaniment of paediatricians. They can provide their support in terms of early diagnosis for example the septic shock in children, prediction of disease severity like in the cases of traumatic brain injury, drug doses and emergency resource management. Lack of research on extensive data on far reaching population, legal and trust issues and unfriendly software’s are the challenges those need to be resolved for utilizing AI at its higher potential in paediatric healthcare.
| Published in | American Journal of Pediatrics (Volume 8, Issue 2) |
| DOI | 10.11648/j.ajp.20220802.11 |
| Page(s) | 51-55 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Artificial Intelligence, Paediatric Emergencies, Childhood Disease, Machine Learning, Algorithms
| [1] | Andrew J Schuman. AI in pediatrics: Past, present, and future. Contemporary PEDS Journal July 2019; 36 (5): 38-41. |
| [2] | Amisha, Paras Malik, Monika Pathania and Vyas kumar Rathaur. Overview of artificial intelligence in medicine. J. of Family Medicine and Primary Care. July 2019; 8 (7): 2328-2331. |
| [3] | Ya-Wan Li. Artificial intelligence in pediatrics. Chinese Medical Journal. Feb 2020; 133 (3): 358-360. |
| [4] | Kokol P, Zavarsnik J. Artificial intelligence and pediatrics: A synthetic mini review. Pediatric dimensions. Oct. 2017; 2 (4): 1-5. |
| [5] | Heike Daldrup. Artificial intelligence applications for pediatric oncology imaging. Pediatr Radiol. 2019 Oct; 49 (11): 1384-1390. |
| [6] | Mardi Gomberg-Maitland and Rogerio Souza. Uncovering Small Secrets in Big Data Sets: How Math Can Identify Biology in Rare Conditions (Pediatric Pulmonary Hypertension). Circulation Research. 2017; 121 (4): 317-319. |
| [7] | Hazlett HC, Gu H, Munsell BC, Elison JT, Wolff JJ, Swanson MR, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature 2017; 542: 348–351. |
| [8] | Aydin M, Hardalaç F, Ural B, Karap S. Neonatal jaundice detection system. J Med Syst 2016; 40: 166–176. |
| [9] | Zhang SJ, Meng P, Zhang J, Jia P, Lin J, Wang X, et al. Machine learning models for genetic risk assessment of infants with non-syndromic orofacial cleft. Genom Proteom Bioinformat 2018; 16: 354–364. |
| [10] | Rajkomar A, Dean J, Kohane I. Machine learning in medicine. New Eng J Med 2019; 380: 1347–1358. |
| [11] | Reismann J, Romualdi A, Kiss N, Minderjahn MI, Kallarackal J, Schad M, Reismann M. Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach. PLoS One. 2019 Sep 25; 14 (9): 1-11. |
| [12] | Michael Schmucker, Martin Haag. Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: Within-Group Comparative Study. JMIR Formative Research [e28345] 2021 Sep; 5 (9). |
| [13] | Gultepe E, Green JP, Nguyen H, Adams J, Albertson T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Informatics Assoc. 2018 Jan; 21 (2): 315–25. |
| [14] | Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, Prediction of In-hospital Mortality in Emergency Department Patients with Sepsis: A Local Big Data-Driven, Machine Learning Approach. J Am Med Informatics Assoc Med. 2016; 23 (3): 269–78. |
| [15] | 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 Informatics Assoc. 2014 March; 21 (2): 326–36. |
| [16] | Aaron J. Masino, Mary Catherine Harris, Daniel Forsyth, Svetlana Ostapenko, Lakshmi Srinivasan, Christopher P. Bonafide, Fran Balamuth, Melissa Schmatz, Robert W. Grundmeier. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data PLoS One [e0212665] 2019; 14 (2). |
| [17] | Shu-Ling Chong, Nan Liu, Sylvaine Barbier, Marcus Eng Hock Ong. Predictive modeling in pediatric traumatic brain injury using machine learning. BMC Med Res Methodol. 2015 Mar 17; 15: 22. |
| [18] | Christopher Pennell, Conner Polet, L Grier Arthur, Harsh Grewal, Stephen Aronoff. Risk assessment for intra-abdominal injury following blunt trauma in children: Derivation and validation of a machine learning model. J Trauma Acute Care Surg. 2020 Jul; 89 (1): 153-159. |
| [19] | Fanelli U., Chiné V., Pappalardo M., Gismondi P., Esposito S. Improving the quality of hospital antibiotic use: Impact on multidrug-resistant bacterial infections in children. Front. Pharmacol. 2020; 11: 745. |
| [20] | Tadahiro Goto, Carlos A Camargo Jr, Mohammad Kamal Faridi, Robert J Freishtat, Kohei Hasegawa. Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage. JAMA Netw Open [e186937].2019 Jan 4; 2 (1). |
| [21] | Swaminathan S., Pasipanodya J. G., Ramachandran G., Kumar A. K. H., Srivastava S., Deshpande D., Nuermberger E., Gumbo T. Drug concentration thresholds predictive of therapy failure and death in children with tuberculosis: Bread crumb trails in random forests. Clin. Infect. Dis. 2016; 63 (3): S63–S74. |
| [22] | Mathieu Beaudoin, Froduald Kabanza, Vincent Nault., Louis Valiquette. An Antimicrobial Prescription Surveillance System that Learns from Experience. AI Magazine. Spring 2014; 35 (1): 15-25. |
| [23] | Shilpa J. Patel, Daniel B. Chamberlain, James M. Chamberlain. A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage. Academic emergency medicine. Nov 2018; 25 (12): 1463-1470. |
| [24] | Marion R Sills, Mustafa Ozkaynak, Hoon Jang Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning. Int J Med Inform [104468] 021 Jul; 151. |
| [25] | Hai-Wei Lee Chun-Tien Tai, Solomon Chih-Cheng Chen, Sheng-Feng Sung. |
| [26] | Predicting return visits to the emergency department for pediatric patients: Applying supervised learning techniques to the Taiwan National Health Insurance Research Database Comput Methods Programs Biomed. Jun 2017; 144: 105-112. |
APA Style
Saeed Abdullah Alzahrani, Abdullah Ahmad Alzahrani, Abdullah Al-Shamrani. (2022). Artificial Intelligence in Paediatric Emergencies: A Narrative Review. American Journal of Pediatrics, 8(2), 51-55. https://doi.org/10.11648/j.ajp.20220802.11
ACS Style
Saeed Abdullah Alzahrani; Abdullah Ahmad Alzahrani; Abdullah Al-Shamrani. Artificial Intelligence in Paediatric Emergencies: A Narrative Review. Am. J. Pediatr. 2022, 8(2), 51-55. doi: 10.11648/j.ajp.20220802.11
AMA Style
Saeed Abdullah Alzahrani, Abdullah Ahmad Alzahrani, Abdullah Al-Shamrani. Artificial Intelligence in Paediatric Emergencies: A Narrative Review. Am J Pediatr. 2022;8(2):51-55. doi: 10.11648/j.ajp.20220802.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajp.20220802.11,
author = {Saeed Abdullah Alzahrani and Abdullah Ahmad Alzahrani and Abdullah Al-Shamrani},
title = {Artificial Intelligence in Paediatric Emergencies: A Narrative Review},
journal = {American Journal of Pediatrics},
volume = {8},
number = {2},
pages = {51-55},
doi = {10.11648/j.ajp.20220802.11},
url = {https://doi.org/10.11648/j.ajp.20220802.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajp.20220802.11},
abstract = {Background: The functionality of Artificial intelligence (AI) in paediatric practices has been gaining more attention for last five years. Since then, researchers have started observing that the techniques are helpful in dealing multiple facets of childhood diseases including emergency like situations. This article has been aimed to discuss the current status of usefulness of AI in paediatric emergencies. Methods: Total 22 research articles have been reviewed. Articles were searched from electronic database like Pubmed, Medline, Google scholar. Artificial intelligence, machine learning, paediatric emergencies, childhood diseases were the key words used to ease the search. Results: Out of 22, 15 were chosen as relatable to paediatric emergency situations per se. After reviewing the available literature, the utility of AI in paediatric emergencies had been discussed under four sub headings: i) Diagnosis; ii) Predictive modelling iii) Assistance in Antimicrobial stewardship iv) Management of emergency department resources. Conclusion: AI and different machine learning techniques have been proven as reliable accompaniment of paediatricians. They can provide their support in terms of early diagnosis for example the septic shock in children, prediction of disease severity like in the cases of traumatic brain injury, drug doses and emergency resource management. Lack of research on extensive data on far reaching population, legal and trust issues and unfriendly software’s are the challenges those need to be resolved for utilizing AI at its higher potential in paediatric healthcare.},
year = {2022}
}
TY - JOUR T1 - Artificial Intelligence in Paediatric Emergencies: A Narrative Review AU - Saeed Abdullah Alzahrani AU - Abdullah Ahmad Alzahrani AU - Abdullah Al-Shamrani Y1 - 2022/04/09 PY - 2022 N1 - https://doi.org/10.11648/j.ajp.20220802.11 DO - 10.11648/j.ajp.20220802.11 T2 - American Journal of Pediatrics JF - American Journal of Pediatrics JO - American Journal of Pediatrics SP - 51 EP - 55 PB - Science Publishing Group SN - 2472-0909 UR - https://doi.org/10.11648/j.ajp.20220802.11 AB - Background: The functionality of Artificial intelligence (AI) in paediatric practices has been gaining more attention for last five years. Since then, researchers have started observing that the techniques are helpful in dealing multiple facets of childhood diseases including emergency like situations. This article has been aimed to discuss the current status of usefulness of AI in paediatric emergencies. Methods: Total 22 research articles have been reviewed. Articles were searched from electronic database like Pubmed, Medline, Google scholar. Artificial intelligence, machine learning, paediatric emergencies, childhood diseases were the key words used to ease the search. Results: Out of 22, 15 were chosen as relatable to paediatric emergency situations per se. After reviewing the available literature, the utility of AI in paediatric emergencies had been discussed under four sub headings: i) Diagnosis; ii) Predictive modelling iii) Assistance in Antimicrobial stewardship iv) Management of emergency department resources. Conclusion: AI and different machine learning techniques have been proven as reliable accompaniment of paediatricians. They can provide their support in terms of early diagnosis for example the septic shock in children, prediction of disease severity like in the cases of traumatic brain injury, drug doses and emergency resource management. Lack of research on extensive data on far reaching population, legal and trust issues and unfriendly software’s are the challenges those need to be resolved for utilizing AI at its higher potential in paediatric healthcare. VL - 8 IS - 2 ER -
Information
Email: