Articial Intelligence - Driven Prediction of Health Issues in Infants - A Review
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Abstract
Advances in technology and data availability have helped in improving the quality of care and in predicting health issues in infants. Currently, Information and Communication technology aids in reaching the essentiality and the applications of infant health to a greater extent. Over a few decades, researchers have dived into sensing and the prediction of Artificial Intelligence (AI) for infant health. Since these healthcare systems deal with large amounts of data, significant development is seen in several computing platforms. AI, including both machine learning (ML) and deep learning (DL), plays a crucial role in the medical industry in the prediction and classification of various infant diseases. The prediction of diseases in infants using extubation readiness and their utility ranges is still lacking. Thus, the present study aims to present a complete review of the adaption of ML and DL approaches to infant health prediction. The current review paper provides a complete overview of the research predicting infant health issues. Effectual comparisons are made among the AI approaches performing infant disease prediction. Furthermore, the paper identifies the research gaps and the future direction of the research in the present domain. A comprehensive form of analysis of the current landscapes involved in predicting infant health issues using AI is presented.
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References
A. D. R. Fern´andez, D. R. Fern´andez and M.T. P. S´anchez, “A decision support system for predicting the treatment of ectopic pregnancies,” International Journal of Medical Informatics, vol. 129, pp. 198-204, 2019.
Y. G. Robi and T. M. Sitote, “Neonatal Disease Prediction Using Machine Learning Techniques,” Journal of Healthcare Engineering, vol. 2023, no. 3567194, 2023.
S. G. Ahmad et al., “Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review,” Sensors (Basel), vol. 22, no. 12:4362, 2022.
J. Yuan et al., “The burden of neonatal diseases attributable to Ambient PM 2.5 in China from 1990 to 2019,” Frontiers in Environmental Science, vol. 10, no. 828408, pp. 1-10, 2022.
U. Purwar, S. Gupta, V. Gautam and S. C. Maurya, “A COMPARATIVE ANALYTICS OF SVM DT NB CLASSIFIER FOR HEART DISEASE PREDICTION IN ML ALGORITHMS,” International Research Journal of Modernization in Engineering Technology and Science, vol. 5, no. 5, pp. 3117-3120, 2023.
P. Shaw, K. Pachpor and S. Sankaranarayanan, “Explainable AI Enabled Infant Mortality Prediction Based on Neonatal Sepsis,” Computer Systems Science & Engineering, vol. 44, no. 1, pp. 311-325, 2023.
A. Choudhury and E. Urena, “Artificial intelligence in neonatal and pediatric intensive care units,” Artificial Intelligence in Clinical Practice, pp. 275-284, 2024.
R. Arnaout, L. Curran, Y. Zhao, J. C. Levine, E. Chinn and A. J. Moon-Grady, “An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease,” Nature Medicine, vol. 27, no. 5, pp. 882-891, 2021.
K. Niha, S. Amutha, and B. Surendiran, “Deep Learning Techniques for Foetal and Infant Data Processing in a Medical Context,” Healthcare Industry 4.0, CRC Press, pp. 19-50, 2023.
I. O. Muniru, J. Grobler, and L. Van Wyk, “Detecting Intra Ventricular Haemorrhage in Preterm Neonates Using LSTM Autoencoders,” Bioinformatics and Biomedical Engineering, Springer, pp. 455-468, 2023.
A. Kingston et al., “Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model,” Age and Ageing, vol. 47, no. 3, pp. 374-380, 2018.
P. Apell and H. Eriksson, “Artificial intelligence (AI) healthcare technology innovations: the current state and challenges from a life science industry perspective,” Technology Analysis & Strategic Management, vol. 35, no. 2, pp. 179-193, 2023.
S. A. Suha and T. F. Sanam, ”Exploring dominant factors for ensuring the sustainability of utilizing artificial intelligence in healthcare decision making: An emerging country context,” International Journal of Information Management Data Insights, vol. 3, no. 1, p. 100170, 2023.
N. Ghaffar Nia, E. Kaplanoglu and A. Nasab, “Evaluation of artificial intelligence techniques in disease diagnosis and prediction,” Discover Artificial Intelligence, vol. 3, no. 5, 2023.
C. Liu, D. Jiao and Z. Liu, “Artificial intelligence (AI)-aided disease prediction,” BIO Integration, vol. 1, no. 3, pp. 130-136, 2020.
G. M. Currie, “Intelligent imaging: artificial intelligence augmented nuclear medicine,”Journal of Nuclear Medicine Technology, vol. 47, no. 3, pp. 217-222, 2019.
J. Yu, S. Park, S.-H. Kwon, C. M. B. Ho, C.-S. Pyo and H. Lee, “AI-based stroke disease prediction system using real-time electromyography signals,” Applied Sciences, vol. 10, no. 19, p. 6791, 2020.
M. S. Singh, P. Choudhary and K. Thongam, “A comparative analysis for various stroke prediction techniques,” Computer Vision and Image Processing, Springer, pp. 98-106, 2020.
M. S. Azam, M. Habibullah, and H. K. J. I. J.o. C. A. Rana, “Performance analysis of various machine learning approaches in stroke prediction,” vol. 175, no. 21, pp. 11-15, 2020.
L. Jiang et al., “A deep learning-based model for prediction of hemorrhagic transformation after stroke,” Brain Pathology, vol. 33, no. 2, p.e13023, 2023.
K. Wang et al., “Deep learning detection of penumbral tissue on arterial spin labeling in stroke,” Stroke, vol. 51, no. 2, pp. 489-497, 2020.
J. Liu, W. Tao, Z. Wang, X. Chen, B. Wu, and M. Liu, “Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning,” Therapeutic Advances in Neurological Disorders, vol. 14, pp. 1-12, 2021.
K. P. Gunasekaran, “Leveraging object detection for the identification of lung cancer,” International Advanced Research Journal in Science, Engineering and Technology, vol.7, no. 5, 2020.
M. Ozcan and S. Peker, “A classification and regression tree algorithm for heart disease modeling and prediction,” Healthcare Analytics, vol.3, p. 100130, 2023.
P. Whig, K. Gupta, N. Jiwani, H. Jupalle, S. Kouser and N. Alam, “A novel method for diabetes classification and prediction with Pycaret,” Microsystem Technologies, vol. 29, pp.1479-1487, 2023.
R. Sawhney, A. Malik, S. Sharma, and V. Narayan, “A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease,” Decision Analytics Journal, vol. 6, p. 100169, 2023.
C. M. Bhatt, P. Patel, T. Ghetia, and P. L. Mazzeo, “Effective heart disease prediction using machine learning techniques,” Algorithms, vol. 16, no. 2, p. 88, 2023.
V. Shorewala, “Early detection of coronary heart disease using ensemble techniques,” Informatics in Medicine Unlocked, vol. 26, p. 100655, 2021.
S. Onnivello, E. Schworer, L. Daunhauer and D. J. Fidler, “Acquisition of cognitive and communication milestones in infants with Down syndrome,” Journal of Intellectual Disability Research, vol. 67, no. 3, pp. 239-253, 2023.
S. R. Kobiljonova, N. N. Jalolov, S. A. Sharipova and G. A. Tashmatova, “Clinical and morphological features of gastroduodenitis in children with saline diathesis,” American Journal of Pedagogical and Educational Research, vol. 10, pp.35-41, 2023.
A. B. Bagheri et al., “Potential applications of artificial intelligence (AI) and machine learning (ML) on diagnosis, treatment, outcome prediction to address health care disparities of chronic limb-threatening ischemia (CLTI),” Seminars in Vascular Surgery, vol. 36, no. 3, pp. 454-459, 2023.
A. Honor´e, D. Forsberg, K. Adolphson, S. Chatterjee, K. Jost and E. Herlenius, “Vital sign-based detection of sepsis in neonates using machine learning,” Acta Paediatrica, vol. 112, no. 4, pp. 686-696, 2023.
J. J. Ashton, A. Young, M. J. Johnson, and R. M.Beattie, “Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities,” Pediatric Research, vol. 93, no. 2, pp. 324-333, 2023.
H. Lu et al., “Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes,” IEEE Rev Biomed Eng., vol. 17, pp. 98-117, 2023.
J. Mukherjee, R. Sharma, P. Dutta, B. J. B. Bhunia and G. E. Reviews, “Artificial intelligence in healthcare: a mastery,” Biotechnology and Genetic Engineering Reviews, vol. 40, no. 3, pp. 1659-1708, 2023.
M. Cord—De Iaco, A. Kimberly, F. Gesualdo, E. Pandolfi, I. Croci and A. E. Tozzi, “Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children,” Frontiers in Pediatrics, vol. 11, p. 1112074, 2023.
R. R. Dixit, “Predicting Fetal Health using Cardiotocograms: A Machine Learning Approach,” Journal of Advanced Analytics in Healthcare Management, vol. 6, no. 1, pp. 43-57, 2022.
R. Jahangir, “CNN-SCNet: A CNN net-based deep learning framework for infant cry detection in household setting,”Engineering Reports, vol. 6, p. e12786, 2023.
S. M. Moghadam et al., “An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation,” Lancet Digit Health, vol. 4, no. 12, pp.e884-e892, 2022.
D. Groos et al., “Development and validation of a deep learning method to predict cerebral palsy from spontaneous movements in infants at high risk,” Jama Network, vol. 5, no. 7, pp. e2221325-e2221325, 2022.
Y. Kim et al., “Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study,” BMC Pediatrics, vol. 23, no. 525, 2023.
S. Huang, J. Yang, S. Fong and Q. Zhao, “Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges,” Cancer Letters, vol. 471, pp. 61-71, 2020.
R. Ahuja, S. C. Sharma and M. Ali, “A diabetic disease prediction model based on classification algorithms,” Annals of Emerging Technologies in Computing (AETiC), vol. 3, no. 3, pp. 44-52, 2019.
D. Khemasuwan and H. G. Colt, “Applications and challenges of AI-based algorithms in the COVID-19 pandemic,” BMJ Journals, vol. 7, no. 2, 2021.
G. Yang, Q. Ye and J. Xia, “Unbox the black-box for the medical explainable AI via multi- modal and multi-centre data fusion: A mini-review, two showcases and beyond,” Information Fusion, vol. 77, pp. 29-52, 2022.
O. Sagi and L. Rokach, “Explainable decision forest: Transforming a decision forest into an interpretable tree,” Information Fusion, vol. 61, pp. 124-138, 2020.
D. Vijlbrief, J. Dudink, W. van Solinge, M. Benders and S. Haitjema, “From computer to bedside, involving neonatologists in artificial intelligence models for neonatal medicine,” Pediatric Research, vol. 93, no. 2, pp. 437-439, 2023.
L. L. Plesner et al., “Autonomous Chest Radio-graph Reporting Using AI: Estimation of Clinical Impact,” Radiology, vol. 307, no. 3, p.e222268, 2023.
J. D. Hughes, P. Chivers and K. Hoti, “The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study,” Journal of Medical Internet Research, vol. 25, p. e41992, 2023.
J. Libon et al., “Remote diagnostic imaging using artificial intelligence for diagnosing hip dysplasia in infants: Results from a mixed methods feasibility pilot study,” Paediatrics & Child Health, vol. 28, pp. 285-290, 2023.
T. C. Kwok et al., “Application and potential of artificial intelligence in neonatal medicine,” Seminars in Fetal and Neonatal Medicine, vol. 27, no. 5, p. 101346, 2022.
C. O. Adegboro, A. Choudhury, O. Asan and M. M. Kelly, “Artificial intelligence to improve health outcomes in the NICU and PICU: a systematic review,” Hospital Pediatrics, vol. 12, no. 1, pp. 93-110, 2022.