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Medicaid Expansion's Impact on Human Immunodeficiency Virus Outcomes in a Nonurban Southeastern Ryan White HIV/AIDS Program Clinic.医疗补助扩大计划对东南部非城市地区瑞安·怀特艾滋病毒/艾滋病项目诊所人类免疫缺陷病毒治疗结果的影响
Open Forum Infect Dis. 2020 Dec 28;8(2):ofaa595. doi: 10.1093/ofid/ofaa595. eCollection 2021 Feb.
2
Machine learning applications in drug development.机器学习在药物研发中的应用。
Comput Struct Biotechnol J. 2019 Dec 26;18:241-252. doi: 10.1016/j.csbj.2019.12.006. eCollection 2020.
3
Multidimensional Sleep and Mortality in Older Adults: A Machine-Learning Comparison With Other Risk Factors.多维睡眠与老年人死亡率：与其他风险因素的机器学习比较。
J Gerontol A Biol Sci Med Sci. 2019 Nov 13;74(12):1903-1909. doi: 10.1093/gerona/glz044.
4
A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy.在接受治疗的 HIV 患者中，病毒载量优于 CD4 细胞计数，可预测死亡率。
BMC Infect Dis. 2019 Feb 15;19(1):169. doi: 10.1186/s12879-019-3781-1.
5
Ending the HIV Epidemic: A Plan for the United States.终结美国的艾滋病流行：一项计划
JAMA. 2019 Mar 5;321(9):844-845. doi: 10.1001/jama.2019.1343.
6
Random Forest Missing Data Algorithms.随机森林缺失数据算法
Stat Anal Data Min. 2017 Dec;10(6):363-377. doi: 10.1002/sam.11348. Epub 2017 Jun 13.
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Trends in racial/ethnic disparities of new AIDS diagnoses in the United States, 1984-2013.1984 - 2013年美国新艾滋病诊断病例的种族/族裔差异趋势
Ann Epidemiol. 2017 May;27(5):329-334.e2. doi: 10.1016/j.annepidem.2017.04.002. Epub 2017 Apr 21.
8
Missing data and multiple imputation in clinical epidemiological research.临床流行病学研究中的缺失数据与多重填补
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9
Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning Approach.美国物质使用障碍治疗项目参与者中与艾滋病毒检测相关的因素：一种机器学习方法。
AIDS Behav. 2017 Feb;21(2):534-546. doi: 10.1007/s10461-016-1628-y.
10
Effect of Patient Navigation With or Without Financial Incentives on Viral Suppression Among Hospitalized Patients With HIV Infection and Substance Use: A Randomized Clinical Trial.有无经济激励措施的患者导航对合并HIV感染和药物使用的住院患者病毒抑制的影响：一项随机临床试验。
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管理重复测量的策略：使用合成随机森林预测住院 HIV 感染者的 HIV 病毒抑制状态。

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

机构信息

Department of Public Health, Miller School of Medicine, University of Miami, Coral Gables, USA.

Soffer Clinical Research Ctr, 1120 NW 14th St, Room 1059, Miami, FL, 33136-2107, USA.

出版信息

AIDS Behav. 2023 Sep;27(9):2915-2931. doi: 10.1007/s10461-023-04015-1. Epub 2023 Feb 5.

DOI:10.1007/s10461-023-04015-1
PMID:36739589
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10403627/
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.

摘要

自 20 世纪 80 年代以来，艾滋病病毒/艾滋病（HIV/AIDS）疫情仍然是一个主要的公共卫生关注点；未经治疗的 HIV 感染会对生活质量产生诸多影响。为了优化患者的健康结果并减少 HIV 传播，本研究关注 HIV 感染者（PLWH）中的弱势群体，并使用纵向或重复测量比较了不同的病毒抑制预测策略。预测病毒抑制的四种方法是：（1）将每个特征的重复测量作为预测因子；（2）仅将特征的初始（基线）值用作预测因子；（3）使用最后观察到的值作为预测因子；（4）使用从特征中估计的增长曲线来创建特征的个体特定预测的增长曲线。本研究表明，在独立的测试数据集上，基于最低错误率，个体特定的增长曲线预测表现最佳。