Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours

References

• United Nations, Department of Economic and Social Affairs, Population Division. World Population Ageing 2020 Highlights. New York: United Nations; 2021.
   Google Scholar
• Fulop T, Larbi A, Witkowski JM, et al. Aging, frailty and age-related diseases. Biogerontology. 2010;11:547–563. doi:10.1007/s10522-010-9287-2
   PubMed Web of Science ®Google Scholar
• Centers for Medicare & Medicaid Services. National health expenditure data; 2021. Available from: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical. Accessed June 4, 2024.
   Google Scholar
• Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–383. doi:10.1016/0021-9681(87)90171-8
   PubMedGoogle Scholar
• Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–829. PMID: 3928249.
   PubMed Web of Science ®Google Scholar
• Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707–710. doi:10.1007/BF01709751
   PubMed Web of Science ®Google Scholar
• Vincent JL, Moreno R, Takala J, et al. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA. 2001;286(14):1754–1758. doi:10.1001/jama.286.14.1754
   PubMed Web of Science ®Google Scholar
• Chester JG, Rudolph JL. Vital signs in older patients: age-related changes. J Am Med Dir Assoc. 2011;12:337–343. doi:10.1016/j.jamda.2010.04.009
   PubMed Web of Science ®Google Scholar
• Cheng HH, Chen FC, Change MW, et al. Difference between elderly and non-elderly patients in using serum lactate level to predict mortality caused by sepsis in the emergency department. Medicine. 2018;97(e0209). doi:10.1097/MD.0000000000010209
   Google Scholar
• Liu NT, Salinas J. Machine Learning for Predicting Outcomes in Trauma. Shock. 2017;48:504–510. doi:10.1097/SHK.0000000000000898
   PubMed Web of Science ®Google Scholar
• Cheng CY, Chiu IM, Zeng WH, et al. Machine Learning Models For Survival And Neurological Outcome Prediction Of Out-Of-Hospital Cardiac Arrest Patients. Biomed Res. Int. 2021:9590131. doi:10.1155/2021/9590131
   PubMed Web of Science ®Google Scholar
• Cheng CY, Chiu IM, Lin CHR, et al. Stepwise regression machine learning models for in-hospital mortality prediction in patients after ST-segment slevation myocardial infarction (STEMI). In: Proceedings of the 2022 IEEE/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD); 2022:14–18. DOI: 10.1109/SNPD54884.2022.10051815.
   Google Scholar
• Nayak T, Chadaga K, Sampathila N, et al. Deep learning based detection of monkeypox virus using skin lesion images. Med Nov Technol Dev. 2023;18:100243. doi:10.1016/j.medntd.2023.100243
   PubMedGoogle Scholar
• Nayak T, Chadaga K, Sampathila N, et al. Detection of Monkeypox from skin lesion images using deep learning networks and explainable artificial intelligence. Appl Math Sci Eng. 2023;31. doi:10.1080/27690911.2023.2225698
   Google Scholar
• Chadaga K, Prabhu S, Sampathila N, et al. Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers. Sci Rep. 2024;14:1783. doi:10.1038/s41598-024-52428-2
   PubMed Web of Science ®Google Scholar
• Awad A, Bader-El-Den M, McNicholas J, Briggs J. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform. 2017;108:185–195. doi:10.1016/j.ijmedinf.2017.10.002
   PubMed Web of Science ®Google Scholar
• Cheng CY, Kung CT, Chen FC, et al. Machine learning models for predicting in-hospital mortality in patient with sepsis: analysis of vital sign dynamics. Front Med. 2022;9:964667. doi:10.3389/fmed.2022.964667
   Web of Science ®Google Scholar
• McCoy A, Das R. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Qual. 2017;6:e000158. doi:10.1136/bmjoq-2017-000158
   PubMedGoogle Scholar
• Rady MY, Smithline HA, Blake H, Nowak R, Rivers E. A comparison of the shock index and conventional vital signs to identify acute, critical illness in the emergency department. Ann Emerg Med. 1994;24:685–690. doi:10.1016/s0196-0644(94)70279-9
   PubMed Web of Science ®Google Scholar
• Stekhoven DJ, Buhlmann P. MissForest--non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28:112–118. doi:10.1093/bioinformatics/btr597
   PubMed Web of Science ®Google Scholar
• Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;4765–4774. doi:10.48550/arXiv.1705.07874
   Google Scholar
• Lundberg SM, Erion G, Lee SI. Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802 03888. 2018. doi:10.48550/arXiv.1802.03888
   Google Scholar
• Liu X, Hu P, Mao Z, et al. Interpretable machine learning model for early prediction of mortality in elderly patients with multiple organ dysfunction syndrome (MODS): a multicenter retrospective study and cross validation. ArXiv abs/2001 10977. 2020. doi:10.48550/arXiv.2001.10977
   Google Scholar
• Ke X, Zhang F, Huang G, Wang A. Interpretable machine learning to optimize early in-hospital mortality prediction for elderly patients with sepsis: a discovery study. Comput Math Methods Med. 2022;4820464. doi:10.1155/2022/4820464
   PubMedGoogle Scholar
• Silva V, Fernandes D, Rêgo A Predicting mortality risk among elderly inpatients with pneumonia: a machine learning approach. In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS); 2022:344–354. DOI: 10.5220/0011043300003179.
   Google Scholar
• L. SC, N. PP, H. SC, et al. Impact of encephalopathy on mortality in the sepsis syndrome. Crit Care Med. 1990;18:801–806. doi:10.1097/00003246-199008000-00001
   PubMed Web of Science ®Google Scholar
• Eidelman LA, Putterman D, Putterman C, Sprung CL. The spectrum of septic encephalopathy: definitions, etiologies, and mortalities. J Am Med Assoc. 1996;275:470–473. PMID: 8627969.
   PubMed Web of Science ®Google Scholar
• Huang KC, Yang Y, Li CJ, et al. Shock index, pediatric age-adjusted predicts morbidity and mortality in children admitted to the intensive care unit. Front Pediatr. 2021;9:727466. doi:10.3389/fped.2021.727466
   PubMed Web of Science ®Google Scholar
• Chung JY, Hsu CC, Chen JH, et al. Shock index predicted mortality in geriatric patients with influenza in the emergency department. Am J Emerg Med. 2019;37:391–394. doi:10.1016/j.ajem.2018.05.059
   PubMed Web of Science ®Google Scholar
• Bedoya AD, Clement ME, Phelan M, Steorts RC, O’Brien C, Goldstein BA. Minimal impact of implemented early warning score and best practice alert for patient deterioration. Crit Care Med. 2019;47:49–55. doi:10.1097/CCM.0000000000003439
   PubMed Web of Science ®Google Scholar
• Escobar GJ, Liu VX, Schuler A, Lawson B, Greene JD, Kipnis P. Automated identification of adults at risk for in-hospital clinical deterioration. New Engl J Med. 2020;383:1951–1960. doi:10.1056/NEJMsa2001090
   PubMed Web of Science ®Google Scholar
• Bassin L, Raubenheimer J, Bell D. The implementation of a real time early warning system using machine learning in an Australian hospital to improve patient outcomes. Resuscitation. 2023;188:109821. doi:10.1016/j.resuscitation.2023.109821
   PubMed Web of Science ®Google Scholar