Strategies of Managing Repeated Measures: Using Synthetic Random Forest to Predict HIV Viral Suppression Status Among Hospitalized Persons with HIV
Abstract 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 compared different predictive strategies for viral suppression using longitudinal or repeated measures. The four methods of predicting viral suppression are (1) including the repeated measures of each feature as predictors, (2) utilizing only the initial (baseline) value of the feature as predictor, (3) using the last observed value as the predictors and (4) using a growth curve estimated from the features to create individual-specific prediction of growth curves as features. This study suggested the individual-specific prediction of the growth curve performed the best in terms of lowest error rate on an independent set of test data..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
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| Enthalten in: |
Aids and behavior - 27(2023), 9 vom: 05. Feb., Seite 2915-2931 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Jingxin [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| Themen: |
HIV/AIDS |
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| Anmerkungen: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s10461-023-04015-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
OLC2144748713 |
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| 520 | |a Abstract 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 compared different predictive strategies for viral suppression using longitudinal or repeated measures. The four methods of predicting viral suppression are (1) including the repeated measures of each feature as predictors, (2) utilizing only the initial (baseline) value of the feature as predictor, (3) using the last observed value as the predictors and (4) using a growth curve estimated from the features to create individual-specific prediction of growth curves as features. This study suggested the individual-specific prediction of the growth curve performed the best in terms of lowest error rate on an independent set of test data. | ||
| 650 | 4 | |a HIV/AIDS | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Predictive model | |
| 650 | 4 | |a Longitudinal measurement | |
| 700 | 1 | |a Pan, Yue |4 aut | |
| 700 | 1 | |a Nelson, Mindy C. |4 aut | |
| 700 | 1 | |a Gooden, Lauren K. |4 aut | |
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| 700 | 1 | |a Rodriguez, Allan E. |4 aut | |
| 700 | 1 | |a Tross, Susan |4 aut | |
| 700 | 1 | |a del Rio, Carlos |4 aut | |
| 700 | 1 | |a Mandler, Raul N. |4 aut | |
| 700 | 1 | |a Feaster, Daniel J. |4 aut | |
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