Bias Reducing Multitask Learning on Mental Health Prediction
Bias Reducing Multitask Learning on Mental Health Prediction
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DOI：
10.1109/acii55700.2022.9953850
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Khadija Zanna;K. Sridhar;Han Yu;Akane Sano
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文献类型：
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作者：
Khadija Zanna;K. Sridhar;Han Yu;Akane Sano
近年来，由于社会上心理健康问题的增加，开发用于心理健康检测或预测的机器学习模型的研究有所增加。有效使用心理健康预测或检测模型可以帮助心理健康从业者比目前更客观地重新定义心理疾病，并在干预措施可能更有效的早期阶段识别疾病。然而，在评估这类机器学习模型的偏差方面仍然缺乏标准，这导致在提供可靠的预测和解决差异方面面临挑战。由于技术困难、高维临床健康数据的复杂性等因素，这种标准的缺乏仍然存在，这对于生理信号尤其如此。这沿着一些生理信号与某些人口特征之间关系的先前证据，重申了探索利用生理信号的心理健康预测模型中的偏差的重要性。在这项工作中，我们的目标是进行公平性分析，并实现一个基于多任务学习的偏见缓解方法对焦虑预测模型使用心电图数据。我们的方法是基于认识的不确定性及其与模型权重和特征空间表示的关系的想法。我们的分析表明，我们的焦虑预测基础模型在年龄，收入，种族以及参与者是否出生在美国方面引入了一些偏见，与重新加权缓解技术相比，我们的偏见缓解方法在减少模型中的偏见方面表现更好。我们对特征重要性的分析也有助于确定心率变异性与多个人口统计学分组之间的关系。
There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in society. Effective use of mental health prediction or detection models can help mental health practitioners re-define mental illnesses more objectively than currently done, and identify illnesses at an earlier stage when interventions may be more effective. However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities. This lack of standards persists due to factors such as technical difficulties, complexities of high dimensional clinical health data, etc., which are especially true for physiological signals. This along with prior evidence of relations between some physiological signals with certain demographic identities restates the importance of exploring bias in mental health prediction models that utilize physiological signals. In this work, we aim to perform a fairness analysis and implement a multi-task learning based bias mitigation method on anxiety prediction models using ECG data. Our method is based on the idea of epistemic uncertainty and its relationship with model weights and feature space representation. Our analysis showed that our anxiety prediction base model introduced some bias with regards to age, income, ethnicity, and whether a participant is born in the U.S. or not, and our bias mitigation method performed better at reducing the bias in the model, when compared to the reweighting mitigation technique. Our analysis on feature importance also helped identify relationships between heart rate variability and multiple demographic groupings.