对临终关怀中的不信任进行建模
Modeling Mistrust in End-of-Life Care
摘要：
在这项工作中，我们使用机器学习派生的信任分数的医生和病人的关系的特点。我们发现，这个分数有统计学意义的种族协会，并通过建模的信任直接，我们发现更强的差异，在照顾比种族分层。我们进一步证明，不信任是更糟糕的结果的指示，但只有微弱的生理创建的严重程度评分。最后，我们描述了情感分析实验表明，患者与更高水平的不信任有更差的经验和互动与他们的照顾者。这项工作是朝着测量医疗保健领域更公平的机器学习迈出的一步。
英文摘要：
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score. We show that this score has statistically significant racial associations, and that by modeling trust directly we find stronger disparities in care than by stratifying on race. We further demonstrate that mistrust is indicative of worse outcomes, but is only weakly associated with physiologically-created severity scores. Finally, we describe sentiment analysis experiments indicating patients with higher levels of mistrust have worse experiences and interactions with their caregivers. This work is a step towards measuring fairer machine learning in the healthcare domain.
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