反事实思维下评估脉搏血氧仪偏差对机器学习的影响
Evaluating the Impact of Pulse Oximetry Bias in Machine Learning under Counterfactual Thinking
AI总结：
本研究通过反事实设计比较使用SaO2与SpO2训练的机器学习模型，发现脉搏血氧仪偏差会降低模型性能，尤其使高估血氧患者的死亡率预测召回率显著下降，导致更多假阴性。
摘要：
医疗保健中的算法偏差反映了现有的数据偏差。然而，驱动不公平的因素并不总是已知的。医疗设备捕获大量数据，但容易出错；例如，脉搏血氧仪高估了肤色较深个体的动脉血氧饱和度，导致更差的结局。这种偏差对机器学习（ML）模型的影响仍不清楚。本研究解决了量化医疗设备偏差在下游机器学习中影响的技术挑战。我们的实验比较了一个“完美世界”——没有脉搏血氧仪偏差，使用SaO2（血气）——与“现实世界”——存在偏差测量，使用SpO2（脉搏血氧仪）。在这种反事实设计下，两个模型使用相同的数据、特征和设置进行训练，唯一不同的是血氧饱和度的测量方法：使用SaO2的模型作为“对照组”，使用SpO2的模型作为“处理组”。血气血氧关联数据集是一个合适的测试平台，包含163,396对几乎同时的SpO2-SaO2配对测量值，并与广泛的临床特征和结局对齐。我们研究了三个分类任务：院内死亡率、未来24小时呼吸SOFA评分，以及SOFA评分增加两分。使用SaO2而非SpO2的模型通常表现出更好的性能。脉搏血氧仪对O2高估超过3%的患者，其死亡率预测召回率显著下降，从0.63降至0.59，P < 0.001。这反映了临床过程中，偏差的脉搏血氧仪读数给临床医生提供了对患者氧合水平的虚假安心。类似的退化也发生在机器学习模型中，脉搏血氧仪偏差导致在预测不良结局时出现更多假阴性。
英文摘要：
Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors; for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a "perfect world", without pulse oximetry bias, using SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2 (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment". The blood-gas oximetry linked dataset was a suitable test-bed, containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 hours, and SOFA score increase by two points. Models using SaO2 instead of SpO2 generally showed better performance. Patients with overestimation of O2 by pulse oximetry of > 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P < 0.001. This mirrors clinical processes where biased pulse oximetry readings provide clinicians with false reassurance of patients' oxygen levels. A similar degradation happened in ML models, with pulse oximetry biases leading to more false negatives in predicting adverse outcomes.
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