Use of a community advisory board to build equitable algorithms for participation in clinical trials: a protocol paper for HoPeNET.
Use of a community advisory board to build equitable algorithms for participation in clinical trials: a protocol paper for HoPeNET.
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DOI：
10.1136/bmjhci-2021-100453
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发表时间：
2022-03
影响因子：
4.1
通讯作者：
Johnson A
中科院分区：
文献类型：
--
作者：
Farmer N;Osei Baah F;Williams F;Ortiz-Chapparo E;Mitchell VM;Jackson L;Collins B;Graham L;Wallen GR;Powell-Wiley TM;Johnson A
少数种族和族裔参与临床试验一直受到不信任和获得医疗保健问题的困扰。机器学习（ML）在临床试验招募和评估中的应用正在兴起。然而，对于来自社会偏见接受者群体的个人来说，使用ML可能会导致创建和使用有偏见的算法。为了最大限度地减少偏见，可以在社区参与过程的指导下设计公平的ML工具，以促进健康公平。霍华德大学与美国国立卫生研究院合作，为研究中代表性不足的种族/民族社区提供公平的临床试验参与（HoPeNET），旨在从社区咨询委员会（CAB）的经验中创建基于ML的基础设施，以提高非裔美国人/黑人在临床试验中的参与。这项三阶段横断面研究（24个月，n=56）将创建一个社区成员和研究调查员的CAB。研究的三个阶段包括：（1）通过定性/定量方法和基于系统的模型构建参与识别临床试验参与的感知障碍/促进因素;（2）CAB会议的操作和（3）预测ML工具和结果评估的开发。从参与者衍生的基于系统的地图中确定的预测因子将用于ML工具开发。我们期望参与者的风险最小。已获得机构审查委员会批准和知情同意，并确保患者保密。
Participation from racial and ethnic minorities in clinical trials has been burdened by issues surrounding mistrust and access to healthcare. There is emerging use of machine learning (ML) in clinical trial recruitment and evaluation. However, for individuals from groups who are recipients of societal biases, utilisation of ML can lead to the creation and use of biased algorithms. To minimise bias, the design of equitable ML tools that advance health equity could be guided by community engagement processes. The Howard University Partnership with the National Institutes of Health for Equitable Clinical Trial Participation for Racial/Ethnic Communities Underrepresented in Research (HoPeNET) seeks to create an ML-based infrastructure from community advisory board (CAB) experiences to enhance participation of African-Americans/Blacks in clinical trials. This triphased cross-sectional study (24 months, n=56) will create a CAB of community members and research investigators. The three phases of the study include: (1) identification of perceived barriers/facilitators to clinical trial engagement through qualitative/quantitative methods and systems-based model building participation; (2) operation of CAB meetings and (3) development of a predictive ML tool and outcome evaluation. Identified predictors from the participant-derived systems-based map will be used for the ML tool development. We anticipate minimum risk for participants. Institutional review board approval and informed consent has been obtained and patient confidentiality ensured.
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影响因子：
3.1
作者：
Robinson, JaMuir M.;Trochim, William M. K.
通讯作者：
Trochim, William M. K.
影响因子：
3.2
作者：
Zeng, Kun;Pan, Zhiwei;Qu, Yingying
通讯作者：
Qu, Yingying
DOI：
10.1136/amiajnl-2014-002887
发表时间：
2015-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Ni Y;Kennebeck S;Dexheimer JW;McAneney CM;Tang H;Lingren T;Li Q;Zhai H;Solti I
通讯作者：
Solti I
影响因子：
2.3
作者：
Goodman MS;Sanders Thompson VL;Johnson CA;Gennarelli R;Drake BF;Bajwa P;Witherspoon M;Bowen D
通讯作者：
Bowen D
影响因子：
4.1
作者：
Claudel, Sophie E.;Ceasar, Joniqua N.;Powell-Wiley, Tiffany M.
通讯作者：
Powell-Wiley, Tiffany M.