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ORIGINAL RESEARCH

Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals

Pages 435-451 | Received 20 Nov 2022, Accepted 27 Jan 2023, Published online: 03 Feb 2023

References

  • UNAIDS. Global HIV and AIDS statistics - 2020 fact sheet; 2020. Available from: https://www.unaids.org/en/resources/fact-sheet#:~:text=GLOBAL%20HIV%20STATISTICS&text=38.0%20million%20%5B31.6%20million–44.5,AIDS-related%20illnesses%20in%202019. Accessed January 27, 2023.
  • STATISTICS, G.H.A.A. Global information and education on HIV and AIDS; 2019. Available from: https://www.avert.org/global-hiv-and-aids-statistics. Accessed January 27, 2023.
  • World Health Organization. HIV/AIDS; 2020.
  • Statistics, G. Global HIV/AIDS statistics; 2021. Available from: https://www.hiv.gov/hiv-basics/overview/data-and-trends/global-statistics. Accessed January 27, 2023.
  • Assefa Y, Hill PS, Van Damme W, et al. Leaving no one behind, lessons from implementation of policies for universal HIV treatment to universal health coverage. Global Health. 2020;16(1):17. doi:10.1186/s12992-020-00549-4
  • Barnabas G, Sibhatu MK, Berhane Y. Antiretroviral therapy program in Ethiopia benefits from virology treatment monitoring. Ethiop J Health Sci. 2017;27(Suppl 1):1–2. doi:10.4314/ejhs.v27i1.1S
  • Hamid A. The impact of viral load monitoring and CD4 in patient taking anti-retroviral treatment at kicukiro health center. World J Pharm Res. 2017;1897–1908. doi:10.20959/wjpr20178-9114
  • Mazrouee S, Little SJ, Wertheim JO. Incorporating metadata in HIV transmission network reconstruction, A machine learning feasibility assessment. PLoS Comput Biol. 2021;17(9):e1009336. doi:10.1371/journal.pcbi.1009336
  • Pimentel V, Pingarilho M, Alves D, et al. Molecular epidemiology of HIV-1 infected migrants followed up in Portugal, trends between 2001–2017. Viruses. 2020;12:3. doi:10.3390/v12030268
  • Qian Y, Wu Z, Chen C, et al. Detection of HIV-1 viral load in tears of HIV/AIDS patients. Infection. 2020;48(6):929–933. doi:10.1007/s15010-020-01508-2
  • Sharma R, Pai C, Kar H. A retrospective analysis of discordant CD4 and viral load responses in HIV patients on anti-retroviral therapy. Int J Sci Res Publ. 2013;3:1–3.
  • Shoko C, Chikobvu D. A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy. BMC Infect Dis. 2019;19(1):169. doi:10.1186/s12879-019-3781-1
  • Stockman J, Friedman J, Sundberg J, et al. Predictive analytics using machine learning to identify ART clients at health system level at greatest risk of treatment interruption in Mozambique and Nigeria. J Acquir Immune Defic Syndr. 2022;90:2. doi:10.1097/QAI.0000000000002947
  • Aavani P, Allen LJS. The role of CD4 T cells in immune system activation and viral reproduction in a simple model for HIV infection. Appl Math Model. 2019;75:210–222. doi:10.1016/j.apm.2019.05.028
  • Søgaard OS. Deciphering the association between HIV-specific immunity and immune reconstitution. EBioMedicine. 2021;67:103350. doi:10.1016/j.ebiom.2021.103350
  • Migueles SA, Connors M. The role of CD4+ and CD8+ T cells in controlling HIV infection. Curr Infect Dis Rep. 2002;4(5):461–467. doi:10.1007/s11908-002-0014-2
  • Tripiciano A, Picconi O, Moretti S, et al. Anti-Tat immunity defines CD4(+) T-cell dynamics in people living with HIV on long-term cART. EBioMedicine. 2021;66:103306. doi:10.1016/j.ebiom.2021.103306
  • Edelman EJ, Rentsch CT, Justice AC. Polypharmacy in HIV, recent insights and future directions. Curr Opin HIV AIDS. 2020;15(2):126–133. doi:10.1097/COH.0000000000000608
  • Takahashi N, Ardeshir A, Holder GE, et al. Comparison of predictors for terminal disease progression in simian immunodeficiency virus/simian-HIV-infected rhesus macaques. Aids. 2021;35(7):1021–1029. doi:10.1097/QAD.0000000000002874
  • Tu W, Johnson E, Fujiwara E, et al. Predictive variables for peripheral neuropathy in treated HIV type 1 infection revealed by machine learning. Aids. 2021;35(11):1785–1793. doi:10.1097/QAD.0000000000002955
  • Javaid M, Haleem A, Pratap Singh R, et al. Significance of machine learning in healthcare, Features, pillars and applications. Int J Intell Netwrk. 2022;3:58–73. doi:10.1016/j.ijin.2022.05.002
  • Secinaro S, Calandra D, Secinaro A, et al. The role of artificial intelligence in healthcare, a structured literature review. BMC Med Inform Decis Mak. 2021;21(1):125. doi:10.1186/s12911-021-01488-9
  • Witten I. Data Mining Practical Machine Learning Tools and Techniques. 2nd ed. Amsterdam, Boston, Heidelberg, London, New york, Oxford, Paris, San diego, San Francisco, Singapore, Sydney, Tokyo: Elsevier; 2010.
  • Singh P. Chapter 5 - Diagnosing of Disease Using Machine Learning, in Machine Learning and the Internet of Medical Things in Healthcare. Academic Press; 2021:89–111.
  • Erickson BJ. Basic artificial intelligence techniques, machine learning and deep learning. Radiol Clin North Am. 2021;59(6):933–940. doi:10.1016/j.rcl.2021.06.004
  • Capitaine L, Genuer R, Thiébaut R. Random forests for high-dimensional longitudinal data. Stat Methods Med Res. 2021;30(1):166–184. doi:10.1177/0962280220946080
  • Duke ER, Williamson BD, Borate B, et al. CMV viral load kinetics as surrogate endpoints after allogeneic transplantation. J Clin Invest. 2021;131:1. doi:10.1172/JCI133960
  • Jamal S, Nikolić N, Mildner M, et al. Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis. Sci Rep. 2020;10:1. doi:10.1038/s41598-019-56847-4
  • Kuhn T, Kaufmann T, Doan NT, et al. An augmented aging process in brain white matter in HIV. Hum Brain Mapp. 2018;39(6):2532–2540. doi:10.1002/hbm.24019
  • Zarei H, Kamyad AV, Heydari AA. Fuzzy modeling and control of HIV infection. Comput Math Methods Med. 2012;2012:893474. doi:10.1155/2012/893474
  • Sajda P. Machine learning for detection and diagnosis of disease. Biomed Eng. 2006;8:537–565.
  • Abirami N, Kamalakannan T, Muthukumaravel A. A study on analysis of various data mining classification techniques on healthcare data. Int J Emerging Technol Adv Eng. 2013;3(7):604–607.
  • Chen S, Owolabi Y, Dulin M, et al. Applying a machine learning modelling framework to predict delayed linkage to care in patients newly diagnosed with HIV in Mecklenburg County, North Carolina, USA. Aids. 2021;35(Suppl 1):S29–s38. doi:10.1097/QAD.0000000000002830
  • Ekpenyong ME, Etebong PI, Jackson TC. Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy. Heliyon. 2019;5(7):e02080. doi:10.1016/j.heliyon.2019.e02080
  • Benitez AE, Musinguzi N, Bangsberg DR, et al. Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda. J Int AIDS Soc. 2020;23(3):e25467. doi:10.1002/jia2.25467
  • Bisaso K, Karungi SA, Kiragga A, et al. A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients. BMC Med Inform Decis Mak. 2018;18:1. doi:10.1186/s12911-018-0659-x
  • Chakraborty S, Roy M, Chatterjee S, et al. Detection of HIV-1 progression phases from transcriptional profiles in ex vivo CD4+ and CD8+ T cells using meta-heuristic supported artificial neural network. Multimed Tools Appl. 2022;81(11):15103–15126. doi:10.1007/s11042-022-12534-7
  • Kamal S, Urata J, Cavassini M, et al. Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence. AIDS Care. 2021;33(4):530–536. doi:10.1080/09540121.2020.1751045
  • Maskew M, Sharpey-Schafer K, De Voux L, et al. Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts. Sci Rep. 2022;12(1):12715. doi:10.1038/s41598-022-16062-0
  • Murnane PM, Ayieko J, Vittinghoff E, et al. Machine learning algorithms using routinely collected data do not adequately predict viremia to inform targeted services in postpartum women living with HIV. J Acquir Immune Defic Syndr. 2021;88(5):439–447. doi:10.1097/QAI.0000000000002800
  • Paul RH, Cho KS, Belden AC, et al. Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy. Aids. 2020;34(5):737–748. doi:10.1097/QAD.0000000000002471
  • Paul RH, Cho KS, Luckett P, et al. Machine learning analysis reveals novel neuroimaging and clinical signatures of frailty in HIV. J Acquir Immune Defic Syndr. 2020;84(4):414–421. doi:10.1097/QAI.0000000000002360
  • Peng X, Zhu B. Different features identified by machine learning associated with the HIV compartmentalization in semen. Infect Genet Evol. 2022;98:105224. doi:10.1016/j.meegid.2022.105224
  • Petersen ML, LeDell E, Schwab J, et al. Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring. J Acquir Immune Defic Syndr. 2015;69(1):109–118. doi:10.1097/QAI.0000000000000548
  • Shi M, Lin J, Wei W, et al. Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China. PLoS Negl Trop Dis. 2022;16(5):e0010388. doi:10.1371/journal.pntd.0010388
  • Wang D, Larder B, Revell A, et al. A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy. Artif Intell Med. 2009;47(1):63–74. doi:10.1016/j.artmed.2009.05.002
  • Yang X, Zhang J, Chen S, et al. Utilizing electronic health record data to understand comorbidity burden among people living with HIV, a machine learning approach. Aids. 2021;35(Suppl 1):S39–s51. doi:10.1097/QAD.0000000000002736
  • Zhang J, Olatosi B, Yang X, et al. Studying patterns and predictors of HIV viral suppression using A big data approach, a research protocol. BMC Infect Dis. 2022;22(1):122. doi:10.1186/s12879-022-07047-5
  • Li B, Li M, Song Y, et al. Construction of machine learning models to predict changes in immune function using clinical monitoring indices in HIV/AIDS patients after 9.9-years of antiretroviral therapy in Yunnan. Front Cell Infect Microbiol. 2022;12:867737. doi:10.3389/fcimb.2022.867737
  • Weissman S, Yang X, Zhang J, et al. Using a machine learning approach to explore predictors of healthcare visits as missed opportunities for HIV diagnosis. Aids. 2021;35(Suppl 1):S7–s18. doi:10.1097/QAD.0000000000002735
  • Pulliam L, Liston M, Sun B, et al. Using neuronal extracellular vesicles and machine learning to predict cognitive deficits in HIV. J Neurovirol. 2020;26(6):880–887. doi:10.1007/s13365-020-00877-6
  • Soogun AO, Kharsany ABM, Zewotir T, et al. Identifying potential factors associated with high HIV viral load in KwaZulu-Natal, South Africa using multiple correspondence analysis and random forest analysis. BMC Med Res Methodol. 2022;22(1):174. doi:10.1186/s12874-022-01625-6
  • Ioannidis JP, Goedert JJ, McQueen PG, et al. Comparison of viral load and human leukocyte antigen statistical and neural network predictive models for the rate of HIV-1 disease progression across two cohorts of homosexual men. J Acquir Immune Defic Syndr Hum Retrovirol. 1999;20(2):129–136. doi:10.1097/00042560-199902010-00004
  • Yashik Singh MM. Support vector machines to forecast changes in CD4 count of HIV-1 positive patients. Sci Res Essays. 2010;5(17):2384–2390.
  • Madigan EA, Miklos Zrinyi OLC, Zrinyi M. Workforce analysis using data mining and linear regression to understand HIV/AIDS prevalence patterns. Hum Resour Health. 2008;6:1–6. doi:10.1186/1478-4491-6-2
  • HEALTH, I.G. Machine Learning for predicting default from HIV services in Mozambique; 2022.
  • Kuteesa R, Bisaso GTA, Susan A, Kiragga A, Castelnuovo B. Karungi, Agnes Kiragga, Barbara Castelnuovo A survey of machine learning applications in HIV clinical research and care. Comput Biol Med. 2017;91:366–371. doi:10.1016/j.compbiomed.2017.11.001
  • Chala TD. Data mining technology enabled anti retroviral therapy (ART) for HIV positive patients in Gondar University Hospital, Ethiopia. Bioinformation. 2019;15(11):790–798. doi:10.6026/97320630015790
  • Kebede M, Zegeye DT, Zeleke BM. Predicting CD4 count changes among patients on antiretroviral treatment, Application of data mining techniques. Comput Methods Programs Biomed. 2017;152:149–157. doi:10.1016/j.cmpb.2017.09.017
  • Nemomsa G, Azath M. Designing a predictive model for antiretroviral regimen at the antiretroviral therapy center in Chiro Hospital, Ethiopia. J Healthc Eng. 2021;2021:1161923. doi:10.1155/2021/1161923
  • Sibanda W, Pretorius P. A review of applications of neural networks in the modeling of HIV epidemic. Int J Comput Appl. 2012;44:16.
  • Romero-Rodríguez D, Ramírez C, Imaz-Rosshandler I, et al. Machine learning-selected variables associated with CD4 T cell recovery under antiretroviral therapy in very advanced HIV infection. Transl Med Commun. 2020;5:1. doi:10.1186/s41231-020-00058-x
  • Federal minstry of health (FMOH). National guidelines for comprehensive HIV prevention, care and treatment; 2017.
  • World Health Organization. Consolidated Guidelines on HIV Prevention, Testing, Treatment, Service Delivery and Monitoring, Recommendations for a Public Health Approach, WHO, Editor. Geneva: World Health Organization; 2021.
  • Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn, machine learning in python. J Mach Learn Res. 2012;12:1.
  • Kulkarni A, Chong D, Batarseh FA. 5 - Foundations of Data Imbalance and Solutions for a Data Democracy, in Data Democracy. Batarseh FA, Yang R, Editors. Academic Press; 2020:83–106.
  • Chawla N, Bowyer KW, Hall LO, et al. SMOTE, synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357. doi:10.1613/jair.953
  • Lemaître G, Nogueira F, Aridas C. Imbalanced-learn, A python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res. 2017;18(1):559–563.
  • Huang C, Li S-X, Caraballo C, et al. Performance metrics for the comparative analysis of clinical risk prediction models employing machine learning. Circ Cardiovasc Qual Outcomes. 2021;14(10):e007526. doi:10.1161/CIRCOUTCOMES.120.007526
  • Li B, Li M, Song Y, et al. Construction of machine learning models to predict changes in immune function using clinical monitoring indices in HIV/AIDS patients after 9.9-years of antiretroviral therapy in Yunnan, China. Front Cell Infect Microbiol. 2022;12:1.