Clinical predictive algorithms are increasingly being used to form the basis for optimal treatment policies--that is, to enable interventions to be targeted to the patients who will presumably benefit most. Despite taking advantage of recent advances in supervised machine learning, these algorithms remain, in a sense, blunt instruments--often being developed and deployed without a full accounting of the causal aspects of the prediction problems they are intended to solve. Indeed, in many settings, including among patients at risk of readmission, the riskiest patients may derive less benefit from a preventative intervention compared to those at lower risk. Moreover, targeting an intervention to a population, rather than limiting it to a small group of high-risk patients, may lead to far greater overall utility if the patients with the most modifiable (or preventable) outcomes across the population could be identified. Based on these insights, we introduce a causal machine learning framework that decouples this prediction problem into causal and predictive parts, which clearly delineates the complementary roles of causal inference and prediction in this problem. We estimate treatment effects using causal forests, and characterize treatment effect heterogeneity across levels of predicted risk using these estimates. Furthermore, we show how these effect estimates could be used in concert with the modeled "payoffs" associated with successful prevention of individual readmissions to maximize overall utility. Based on data taken from before and after the implementation of a readmissions prevention intervention at Kaiser Permanente Northern California, our results suggest that nearly four times as many readmissions could be prevented annually with this approach compared to targeting this intervention using predicted risk.
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Mederrata（梅德拉塔）；National Institutes of Health（美国国立卫生研究院）；University of Vermont（佛蒙特大学）
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
Mederrata Research and Sound Prediction（Mederrata研究与声音预测公司）；NIH NIDDK（美国国立卫生研究院国家糖尿病、消化与肾脏疾病研究所）；Mederrata Research（Mederrata研究公司）；Blue Health Intelligence（蓝色健康智能公司）；Walter Reed Army Institute of Research（沃尔特·里德陆军研究所）；Sound Prediction and Mederrata Research（声音预测与Mederrata研究公司）
Days Alive at Home vs Out of Hospital: Why the Difference Matters for Trial Endpoints
MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计单元）；Papworth Trials Unit Collaboration, Royal Papworth Hospital（皇家帕普沃斯医院帕沃斯试验单位合作组织）；University of Cambridge（剑桥大学）；Department of Anaesthesia, Royal Papworth Hospital（皇家帕普沃斯医院麻醉科）
Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach
University of Washington Tacoma（塔科马华盛顿大学）；Ghent University（根特大学）；Multicare Health System（多凯尔医疗系统）
降低充血性心力衰竭（CHF）患者在出院后30天内再入院的风险非常重要，因为这种再入院不仅费用昂贵，而且是提供者护理和治疗质量的关键指标。准确预测再入院的风险可以使医院识别高风险患者，并最终通过识别在许多情况下导致这种再入院的因素来提高护理质量。在本文中，我们研究的问题，预测风险的再入院作为一个监督学习问题，使用多层分类方法。与我们的方法相反，早期的文献没有充分地试图通过建立直接预测模型来评估30天再入院的风险值。我们首先将问题分为不同的阶段，（a）一般风险（b）60天内的风险（c）30天内的风险，然后为每个阶段构建合适的分类器，从而提高使用多层决策准确预测风险的能力。我们的方法的优点是，我们可以使用不同的分类模型的子任务，更适合各自的问题。此外，与一次性单层方法相比，可以使用不同的特征和训练数据来解决每个子任务，从而导致高度自信的诊断或风险。对Multicare Health Systems的实际医院患者记录数据的实验评估表明，与之前的尝试相比，我们的模型在预测CHF患者出院后30天内的再入院风险方面明显更好。
Gender disparities in rehospitalisations after coronary artery bypass grafting: evidence from a sparse functional causal mediation analysis of the MIMIC-IV data
