Application of machine-learning techniques in classification of HIV medical care status for people living with HIV in South Carolina.
Journal
AIDS (London, England)
ISSN: 1473-5571
Titre abrégé: AIDS
Pays: England
ID NLM: 8710219
Informations de publication
Date de publication:
01 05 2021
01 05 2021
Historique:
entrez:
19
4
2021
pubmed:
20
4
2021
medline:
20
5
2021
Statut:
ppublish
Résumé
Ending the HIV epidemic requires innovative use of data for intelligent decision-making from surveillance through treatment. This study sought to examine the usefulness of using linked integrated PLWH health data to predict PLWH's future HIV care status and compare the performance of machine-learning methods for predicting future HIV care status for SC PLWH. We employed supervised machine learning for its ability to predict PLWH's future care status by synthesizing and learning from PLWH's existing health data. This method is appropriate for the nature of integrated PLWH data because of its high volume and dimensionality. A data set of 8888 distinct PLWH's health records were retrieved from an integrated PLWH data repository. We experimented and scored seven representative machine-learning models including Bayesian Network, Automated Neural Network, Support Vector Machine, Logistic Regression, LASSO, Decision Trees and Random Forest to best predict PLWH's care status. We further identified principal factors that can predict the retention-in-care based on the champion model. Bayesian Network (F = 0.87, AUC = 0.94, precision = 0.87, recall = 0.86) was the best predictive model, followed by Random Forest (F = 0.78, AUC = 0.81, precision = 0.72, recall = 0.85), Decision Tree (F = 0.76, AUC = 0.75, precision = 0.70, recall = 0.82) and Neural Network (cluster) (F = 0.75, AUC = 0.71, precision = 0.69, recall = 0.81). These algorithmic applications of Bayesian Networks and other machine-learning algorithms hold promise for predicting future HIV care status at the individual level. Prediction of future care patterns for SC PLWH can help optimize health service resources for effective interventions. Predictions can also help improve retention across the HIV continuum.
Identifiants
pubmed: 33867486
doi: 10.1097/QAD.0000000000002814
pii: 00002030-202105011-00003
pmc: PMC8162887
mid: NIHMS1669689
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
S19-S28Subventions
Organisme : NIAID NIH HHS
ID : R01 AI127203
Pays : United States
Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Références
Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy F, Godbole SV. HPTN 052 Study Team. Prevention of HIV-1 infection with early antiretroviral therapy . New Engl J Med 2011; 365:493–505.
Shrestha RK, Gardner L, Marks G, Craw J, Malitz F, Giordano TP, Mugavero M. (2015). Estimating the cost of increasing retention in care for HIV-infected patients: results of the CDC/HRSA retention in care trial . J Acquir Immune Defic Syndr 1999; 68:345–350.
Centers for Disease Control and Prevention. Understanding the HIV care continuum. 2019. Understanding the HIV Care Continuum. Available at: https://www.cdc.gov/hiv/policies/continuum.html . [Accessed 17 June 2020]
Mugavero MJ, Amico KR, Horn T, Thompson MA. The state of engagement in HIV care in the United States: from cascade to continuum to control . Clin Infect Dis 2013; 57:1164–1171.
Modi R, Amico KR, Knudson A, Westfall AO, Keruly J, Crane HM, et al. Assessing effects of behavioral intervention on treatment outcomes among patients initiating HIV care: Rationale and design of iENGAGE intervention trial . Contemp Clin Trials 2018; 69:48–54.
Crepaz N, Dong X, Wang X, Hernandez AL, Hall HI. Racial and ethnic disparities in sustained viral suppression and transmission risk potential among persons receiving HIV care—United States . Morbid Mortal Wkly Rep 2014; 67:113.
SC Department of Health and Environmental Control (DHEC) An Epidemiologic Profile of HIV and AIDS in South Carolina 2019. Available at: https://scdhec.gov/sites/default/files/media/document/2019-Epi-Profile.pdf . [Accessed 17 June 2020]
Edun B, Iyer M, Albrecht H, Weissman S. The South Carolina HIV cascade of care . South Med J 2015; 108:670–674.
White House Office of National, AIDS Policy. National HIV/AIDS strategy for the United States: updated to 2020 . Washington, DC: White House Office of National AIDS Policy; 2015.
Lall P, Lim SH, Khairuddin N, Kamarulzaman A. An urgent need for research on factors impacting adherence to and retention in care among HIV-positive youth and adolescents from key populations . J Int AIDS Soc 2015; 18:19393.
Tripathi A, Youmans E, Gibson JJ, Duffus WA. The impact of retention in early HIV medical care on viro-immunological parameters and survival: a statewide study . AIDS Res Hum Retroviruses 2011; 27:751–758.
Hall HI, Gray KM, Tang T, Li J, Shouse L, Mermin J. Retention in care of adults and adolescents living with HIV in 13 US areas . JAIDS J Acquir Immune Defic Syndr 2012; 60:77–82.
Poteat T, Hanna DB, Rebeiro PF, Klein M, Silverberg MJ, Eron JJ, et al. Characterizing the human immunodeficiency virus care continuum among transgender women and cisgender women and men in clinical care: a retrospective time-series analysis . Clin Infect Dis 2020; 70:1131–1138.
Dailey AF, Johnson AS, Wu B. HIV care outcomes among blacks with diagnosed HIV—United States . MMWR Morb Mortal Wkly Rep 2017; 66:97.
Dasgupta S, Oster AM, Li J, et al. Disparities in consistent retention in HIV care—11 states and the District of Columbia . Morb Mortal Wkly Rep 2016; 65:77–82.
Schranz AJ, Barrett J, Hurt CB, Malvestutto C, Miller WC. Challenges facing a rural opioid epidemic: treatment and prevention of HIV and hepatitis C . Curr HIV/AIDS Rep 2018; 15:245–254.
Thompson MA, Mugavero MJ, Amico KR, Cargill VA, Chang LW, Gross R, et al. Guidelines for improving entry into and retention in care and antiretroviral adherence for persons with HIV: evidence-based recommendations from an International Association of Physicians in AIDS Care panel . Ann Intern Med 2012; 156:817–833.
Dombrowski JC, Simoni JM, Katz DA, Golden MR. Barriers to HIV care and treatment among participants in a public health HIV care relinkage program . AIDS Patient Care STDS 2015; 29:279–287.
Coyle RP, Schneck CD, Morrow M, Coleman SS, Gardner EM, Zheng JH, et al. Engagement in mental healthcare is associated with higher cumulative drug exposure and adherence to antiretroviral therapy . AIDS Behav 2019; 23:3493–3502.
Giordano TP, Gifford AL, White AC Jr, Suarez-Almazor ME, Rabeneck L, Hartman C, et al. Retention in care: a challenge to survival with HIV infection . Clin Infect Dis 2007; 44:1493–1499.
Nelson JA, Kinder A, Johnson AS, Hall HI, Hu X, Sweet D, et al. Differences in selected HIV care continuum outcomes among people residing in rural, urban, and metropolitan areas—28 US jurisdictions . J Rural Heal 2018; 34:63–70.
Philbin MM, Feaster DJ, Gooden L, Duan R, Das M, Jacobs P, et al. The north-south divide: substance use risk, care engagement, and viral suppression among hospitalized human immunodeficiency virus--infected patients in 11 US cities . Clin Infect Dis 2019; 68:146–149.
Rebeiro PF, Gange SJ, Horberg MA, Abraham AG, Napravnik S, Samji H, et al. North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD). Geographic variations in retention in care among HIV-infected adults in the United States . PLoS One 2016; 11:e0146119.
Hartzler B, Dombrowski JC, Williams JR, Crane HM, Eron JJ, Geng EH, et al. Influence of substance use disorders on 2-year HIV care retention in the United States . AIDS Behav 2018; 22:742–751.
Mugavero MJ, Lin H-Y, Willig JH, Westfall AO, Ulett KB, Routman JS, et al. Missed visits and mortality among patients establishing initial outpatient HIV treatment . Clin Infect Dis 2009; 48:248–256.
Jain KM, Maulsby C, Brantley M, Kim JJ, Zulliger R, Holtgrave DR. SIF Intervention Team. Cost and cost threshold analyses for 12 innovative US HIV linkage and retention in care programs . AIDS Care 2016; 28:1199–1204.
Rana AI, Mugavero MJ. How big data science can improve linkage and retention in care . Infect Dis Clin 2019; 33.3:807–815.
Olatosi B, Zhang J, Weissman S, Hu J, Haider MR, Li X. Using Big Data analytics to improve HIV medical care utilisation in South Carolina: a study protocol . BMJ Open 2019; 9:e027688.
Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology . Clin Infect Dis 2018; 66:149–153.
Johnson AS, Johnson SD, Hu S, Li J, Yu C, Wu B, et al. Monitoring selected national HIV prevention and care objectives by using HIV surveillance data: United States and 6 dependent areas, 2017. 2019.
Feelders A. Handling missing data in trees: surrogate splits or statistical imputation? In European Conference on Principles of Data Mining and Knowledge Discovery (pp 329-334) . Berlin, Heidelberg: Springer; 1999.
Zhou XH, Eckert GJ, Tierney WM. Multiple imputation in public health research . Stat Med 2001; 20:1541–1549.
Fushiki T. Estimation of prediction error by using K-fold cross-validation . Stat Comput 2011; 21:137–146.
Ahmad LG, Eshlaghy AT, Poorebrahimi A, Ebrahimi M, Razavi AR. Using three machine learning techniques for predicting breast cancer recurrence . J Health Med Inform 2013; 4:3.
SAS Cary Documentation: Bayesian Network. Available at: https://documentation.sas.com/?activeCdc=vdmmlcdc&cdcId=capcdc&cdcVersion=8.5&docsetId=vdmmlref&docsetTarget=n06li68bxx073yn1eujtwuxe1dg3&locale=en . [Accessed 17 June 2020]
SAS Fast supervised learning. Available at: https://documentation.sas.com/?docsetId=vdmmlref&docsetTarget=p1l7tl7hddl0lon138uretl5muac.htm&docsetVersion=8.4&locale=en . [Accessed 17 June 2020]
Velikova MV, Terwisscha van Scheltinga JA, Lucas PJ, Spaanderman M. Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare 2014; 55 (Pt 1):59–73.
Bayat S, Cuggia M, Rossille D, Kessler M, Frimat L. Comparison of Bayesian network and decision tree methods for predicting access to the renal transplant waiting list In MIE 2009; 150:600–604.
Lappenschaar M, Hommersom A, Lucas PJ, Lagro J, Visscher S. Multilevel Bayesian networks for the analysis of hierarchical healthcare data . Artificial Intelligence Med 2013; 57:171–183.
Sordo M. Introduction to neural networks in healthcare. Open Clinical knowledge management for medical care. 2002.
Ma F, Chitta R, Zhou J, You Q, Sun T, Gao J. Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining; 2017, August; pp. 1903--1911.
O’Neill TJ, Penm J, Penm J. A subset polynomial neural networks approach for breast cancer diagnosis . Int J Electron Healthc 2007; 3:293–302.
Karan O, Bayraktar C, Gümüşkaya H, Karlik B. Diagnosing diabetes using neural networks on small mobile devices . Expert Syst Applications 2012; 39:54–60.
Choi E, Bahadori MT, Kulas JA, Schuetz A, Stewart WF, Sun J. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism . arXiv preprint 2016; arXiv:1608.05745.
Samanta S, Das S. A fast supervised method of feature ranking and selection for pattern classification. In: International Conference on Pattern Recognition and Machine Intelligence. Springer, Berlin, Heidelberg. 2009, pp. 80–85.
Razzaghi T, Roderick O, Safro I, Marko N. Fast imbalanced classification of healthcare data with missing values. In 2015 18th International Conference on Information Fusion (Fusion), IEEE, 2015 July. pp. 774–781.
Gordon L. Using classification and regression trees (CART) in SAS® enterprise miner TM for applications in public health. In: SAS Global Forum, 2013 April, vol. 2013, p. 2013.
García MNM, Herráez JCB, Barba MS, Hernández FS. Random forest based ensemble classifiers for predicting healthcare-associated infections in intensive care units. In Distributed Computing and Artificial Intelligence, 13th International Conference. Cham: Springer. 2016. pp. 303–311.
Ali J, Khan R, Ahmad N, Maqsood I. Random forests and decision trees . Int J Computer Science Issues (IJCSI) 2012; 9:272.
Razzaghi T, Roderick O, Safro I, Marko N. Multilevel weighted support vector machine for classification on healthcare data with missing values . PloS One 2016; 11:e0155119.
Naraei P, Abhari A, Sadeghian A. Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data. In: 2016 Future Technologies Conference (FTC), IEEE. 2016 December. pp. 848--852.
Son Y-J, KimF HG, Kim EH, Choi S, LeeF SK. Application of support vector machine for prediction of medication adherence in heart failure patients . Healthc Inform Res 2010; 16:253–259.
Lee SK, Kang BY, Kim HG, Son YJ. Predictors of medication adherence in elderly patients with chronic diseases using support vector machine models . Healthc Inform Res 2013; 19:33–41.
Wu J, Roy J, Stewart WF. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches . Med Care 2010; 48: (6 Suppl): S106–S113.
Haas LR, Takahashi PY, Shah ND, Stroebel RJ, Bernard ME, Finnie DM, Naessens JM. Risk-stratification methods for identifying patients for care coordination . Am J Manag Care 2013; 19:725–732.
Plis K, Bunescu R, Marling C, Shubrook J, Schwartz F. A machine learning approach to predicting blood glucose levels for diabetes management,’ Modern Artificial Intelligence for Health Analytics. Papers from the AAAI-14. 2014.
Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset . J Am Med Inform Assoc 2017; 24:361–370.
Bulsara SM, Wainberg ML, Newton-John TR. Predictors of adult retention in HIV care: a systematic review . AIDS Behav 2018; 22:752–764.