Strategies of Managing Repeated Measures: Using Synthetic Random Forest to Predict HIV Viral Suppression Status Among Hospitalized Persons with HIV

The HIV/AIDS epidemic remains a major public health concern since the 1980s; untreated HIV infection has numerous consequences on quality of life. To optimize patients’ health outcomes and to reduce HIV transmission, this study focused on vulnerable populations of people living with HIV (PLWH) and c...

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Veröffentlicht in:	AIDS and behavior 2023-09, Vol.27 (9), p.2915-2931
Hauptverfasser:	Liu, Jingxin, Pan, Yue, Nelson, Mindy C., Gooden, Lauren K., Metsch, Lisa R., Rodriguez, Allan E., Tross, Susan, del Rio, Carlos, Mandler, Raul N., Feaster, Daniel J.
Format:	Artikel
Sprache:	eng
Schlagworte:	Acquired immune deficiency syndrome Acquired Immunodeficiency Syndrome Addictive behaviors AIDS At risk populations Behavior Clinical trials Disease transmission Drug abuse Drug use Growth curves Health Psychology Hepatitis C HIV HIV Infections - diagnosis HIV Infections - epidemiology Hospitalization Hospitals Human immunodeficiency virus Humans Infectious Diseases Machine learning Medicine Medicine & Public Health Monetary incentives Original Paper Patients Population studies Predictions Public Health Quality of Life Random Forest Research Design Substance abuse treatment Variables Within-subjects design
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