arXiv 2411.080482024-11-03cs.LGstat.AP

使用机器学习预测学习障碍与多重长期健康状况患者的公平住院时长

Equitable Length of Stay Prediction for Patients with Learning Disabilities and Multiple Long-term Conditions Using Machine Learning

Loughborough University（拉夫堡大学）；Swansea University Medical School（斯旺西大学医学院）；University of Leicester（莱斯特大学）；Leicestershire Partnership NHS Trust（莱斯特郡合作 NHS 信托机构）

Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

备注：: 13 pages of article with 9 figures; Supplementary material follows after article with 27 pages and 27 figures

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AI总结：

本研究利用SAIL Databank的EHR数据，应用机器学习模型预测威尔士9,618名学习障碍及长期病患的住院时长，并通过阈值优化等偏差缓解算法减少不同种族间的性能差异，实现了更公平的预测。

摘要：

正如英国及其他国家发表的研究报告所述，与普通公众相比，患有学习障碍的人群具有更高的死亡率和过早死亡率。本研究利用来自SAIL Databank的电子健康记录（EHR）数据源，分析了威尔士人群中9,618名确诊患有学习障碍和长期健康状况患者的住院情况。我们描述了研究队列的人口统计学特征、长期健康状况的患病率、用药史、就诊史以及生活方式史，并应用机器学习模型来预测该队列的住院时长。随机森林（RF）模型在男性中取得了0.759的曲线下面积（AUC）和0.224的假阴性率，在女性中取得了0.756的AUC和0.229的假阴性率，并在男性和女性中分别实现了0.690和0.689的平衡准确率。在检查了模型在不同种族群体间的表现后，我们应用了两种偏差缓解算法（阈值优化和使用指数梯度的reductions算法）来最小化性能差异。阈值优化算法的表现优于reductions算法，在不同种族的男性队列中，其假阳性率和平衡准确率达到了更低的波动范围。本研究展示了在EHR数据源上应用带有有效偏差缓解方法的机器学习模型的潜力，通过解决群体间的数据不平衡问题，实现对住院时长的公平预测。

英文摘要：

People with learning disabilities have a higher mortality rate and premature deaths compared to the general public, as reported in published research in the UK and other countries. This study analyses hospitalisations of 9,618 patients identified with learning disabilities and long-term conditions for the population of Wales using electronic health record (EHR) data sources from the SAIL Databank. We describe the demographic characteristics, prevalence of long-term conditions, medication history, hospital visits, and lifestyle history for our study cohort, and apply machine learning models to predict the length of hospital stays for this cohort. The random forest (RF) model achieved an Area Under the Curve (AUC) of 0.759 (males) and 0.756 (females), a false negative rate of 0.224 (males) and 0.229 (females), and a balanced accuracy of 0.690 (males) and 0.689 (females). After examining model performance across ethnic groups, two bias mitigation algorithms (threshold optimization and the reductions algorithm using an exponentiated gradient) were applied to minimise performance discrepancies. The threshold optimizer algorithm outperformed the reductions algorithm, achieving lower ranges in false positive rate and balanced accuracy for the male cohort across the ethnic groups. This study demonstrates the potential of applying machine learning models with effective bias mitigation approaches on EHR data sources to enable equitable prediction of hospital stays by addressing data imbalances across groups.

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