可视化基于机器学习的产后抑郁风险预测,用于普通观众
Pooja M Desai1, Sarah Harkins2, Saanjaana Rahman3
1Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
Journal of the American Medical Informatics Association : JAMIA
|October 17, 2023
概括
不同的机器学习 (ML) 衍生产后抑郁风险得分格式并不影响患者分类的准确性. 然而,较高的风险水平增加了感知风险,信任和治疗协议,梯度数线是首选的格式.
科学领域:
- 医疗信息学 医疗信息学
- 医疗决策的制定 医疗决策的制定
- 患者沟通 患者沟通
背景情况:
- 机器学习 (ML) 模型越来越多地用于预测健康风险,例如产后抑郁症 (PPD).
- 对患者有效地传达这些ML衍生的风险得分对于明智的决策和护理参与至关重要.
- 呈现复杂健康信息的最佳格式,特别是风险评分,仍然是积极研究的领域.
研究的目的:
- 评估不同的视觉和文本格式对 ML 衍生的 PPD 风险评分的影响如何影响患者的理解和行动.
- 评估这些格式对主要结果 (行动分类) 和次要结果 (寻求护理的意图,感知风险,信任和偏好) 的影响.
主要方法:
- 一项在线调查对504名18-45岁的英语女性进行了调查.
- 参与者接触到ML衍生的PPD风险信息,呈现在四种格式:仅文本,仅数字,梯度数列和细分数列.
- 收集了关于患者对推行动的分类,寻求护理的意图,感知风险,信任和格式偏好的数据.
主要成果:
- 在所有呈现格式中,患者对建议行动的分类准确度很高 (93%).
- 增加风险严重程度显著提高了感知风险,对医疗保健提供者的信任,以及对治疗建议的同意.
- 虽然所有格式都产生了很高的分类准确性,但格式如何影响感知到的风险,信任和行为意图存在不一致;梯度数线是最喜欢的格式 (43%).
结论:
- 虽然陈述格式并没有显著改变ML衍生的PPD风险得分的患者分类的准确性,但所呈现的风险水平是患者感知和参与的关键驱动因素.
- 医疗保健提供者和研究人员应根据预期的患者结果仔细选择数据可视化方法,考虑到梯度数线可能会增强患者的偏好.
- 未来的研究应该探索各种格式如何影响患者的信任和行为意图对ML驱动的健康预测的细微差别.
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