美国芝加哥大学Ziad Obermeyer课题组取得一项新突破。他们提出一种减少服务不足人群中无法解释的疼痛差异的算法。2021年1月13日出版的《自然-医学》杂志发表了这项成果。

他们使用深度学习方法来测量骨关节炎的严重程度，方法是使用膝盖X射线来预测患者经受的疼痛。他们证明了这种方法可以极大地减少无法解释的种族差异。相对于放射科医生分级的标准严重性指标（仅占疼痛中种族差异的9％（95％置信区间（CI），3-16％）），这种算法预测占差异43％，或4.7倍以上（ 95％CI（3.2–11.8×），对于低收入和教育程度较低的患者，结果相似。这表明，未得到充分服务的患者疼痛大部分来自膝盖内的因素，而这些因素并未在标准的X射线严重程度中得到反映。

他们证明了该算法减少无法解释的差异的能力是源于群体的种族和社会经济多样性。由于算法严重性指标可以更好地捕获服务不足的患者疼痛，而严重性指标会影响治疗决策，因此该算法预测可能会纠正关节置换术等治疗方法的差异。

据悉，社会服务水平低的人群会遭受更高的痛苦。这些差异甚至在控制了骨关节炎等疾病的客观严重性之后仍然存在，而这种严重程度是由人类医生使用医学图像进行分级的，从而增加了患者未得到充分服务患者疼痛的可能性源自膝盖以外的因素，例如压力。

附：英文原文

Title: An algorithmic approach to reducing unexplained pain disparities in underserved populations

Author: Emma Pierson, David M. Cutler, Jure Leskovec, Sendhil Mullainathan, Ziad Obermeyer

Issue&Volume: 2021-01-13

Abstract: Underserved populations experience higher levels of pain. These disparities persist even after controlling for the objective severity of diseases like osteoarthritis, as graded by human physicians using medical images, raising the possibility that underserved patients’ pain stems from factors external to the knee, such as stress. Here we use a deep learning approach to measure the severity of osteoarthritis, by using knee X-rays to predict patients’ experienced pain. We show that this approach dramatically reduces unexplained racial disparities in pain. Relative to standard measures of severity graded by radiologists, which accounted for only 9% (95% confidence interval (CI), 3–16%) of racial disparities in pain, algorithmic predictions accounted for 43% of disparities, or 4.7× more (95% CI, 3.2–11.8×), with similar results for lower-income and less-educated patients. This suggests that much of underserved patients’ pain stems from factors within the knee not reflected in standard radiographic measures of severity. We show that the algorithm’s ability to reduce unexplained disparities is rooted in the racial and socioeconomic diversity of the training set. Because algorithmic severity measures better capture underserved patients’ pain, and severity measures influence treatment decisions, algorithmic predictions could potentially redress disparities in access to treatments like arthroplasty.

DOI: 10.1038/s41591-020-01192-7

Source: https://www.nature.com/articles/s41591-020-01192-7