[论文解读] Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models

Jialiang Jiang, Sharon Hewner+1 more|arXiv (Cornell University)|Apr 3, 2018

Child Nutrition and Water Access参考文献 14被引用 3

一句话总结

本文提出一种基于树状结构组LASSO的分层稀疏性正则化方法，用于构建可解释的逻辑回归模型，以预测患者疾病史和人口统计学特征下的住院再入院风险。通过利用ICD-9-CM疾病编码的层级结构，该模型在性能上表现优异（AUC ≈ 0.75），并识别出关键风险因素，包括院内感染、精神健康状况、物质滥用，以及营养不良和缺乏住房等社会经济因素，为临床决策支持提供可操作的见解。

ABSTRACT

Hospital readmissions have become one of the key measures of healthcare quality. Preventable readmissions have been identified as one of the primary targets for reducing costs and improving healthcare delivery. However, most data driven studies for understanding readmissions have produced black box classification and predictive models with moderate performance, which precludes them from being used effectively within the decision support systems in the hospitals. In this paper we present an application of structured sparsity-inducing norms for predicting readmission risk for patients based on their disease history and demographics. Most existing studies have focused on hospital utilization, test results, etc., to assign a readmission label to each episode of hospitalization. However, we focus on assigning a readmission risk label to a patient based on their disease history. Our emphasis is on interpreting the models to improve the understanding of the readmission problem. To achieve this, we exploit the domain induced hierarchical structure available for the disease codes which are the features for the classification algorithm. We use a tree based sparsity-inducing regularization strategy that explicitly uses the domain hierarchy. The resulting model not only outperforms standard regularization procedures but is also highly sparse and interpretable. We analyze the model and identify several significant factors that have an effect on readmission risk. Some of these factors conform to existing beliefs, e.g., impact of surgical complications and infections during hospital stay. Other factors, such as the impact of mental disorder and substance abuse on readmission, provide empirical evidence for several pre-existing but unverified hypotheses. The analysis also reveals previously undiscovered connections such as the influence of socioeconomic factors like lack of housing and malnutrition.

研究动机与目标

• 开发一种基于患者疾病史和人口统计学数据的可解释性再入院风险预测模型。
• 通过增强模型的透明度和可解释性，克服黑箱模型在临床决策支持中的局限性。
• 利用ICD-9-CM疾病编码的层级结构，以提升特征选择和模型性能。
• 识别出超越院内事件的临床意义明确且可操作的风险因素，如行为与社会经济因素。
• 通过模型解释，实现数据驱动、基于证据的策略，以减少可避免的再入院。

提出的方法

• 本研究采用在树状结构分层组LASSO范数正则化下的逻辑回归模型，以在ICD-9-CM疾病编码层级上强制实现稀疏性。
• 利用ICD-9-CM编码的层级结构作为先验信息，指导特征选择，促进在相关时选择整个疾病类别。
• 模型基于纽约州Medicaid索赔数据进行训练，涵盖超过100万名患者和18,000种可能的诊断编码。
• 通过识别最具预测力的疾病编码组和个体编码，增强模型的可解释性。
• 对患有特定慢性病（如糖尿病、COPD）的患者子群体进行分析，以识别疾病特异性风险因素。
• 使用AUC等标准指标评估模型性能，并与标准L1和组LASSO正则化方法进行比较。

实验结果

研究问题

• RQ1 如何通过分层稀疏性正则化提升再入院预测模型的可解释性与性能？
• RQ2 在利用ICD-9-CM层级结构时，哪些疾病编码及其编码组对住院再入院风险最具预测力？
• RQ3 哪些非临床因素——如行为或社会经济状况——显著影响再入院风险？
• RQ4 在具有特定慢性病的子群体中，风险因素有何差异？
• RQ5 结构化稀疏性模型能否揭示索赔数据中先前未知或被低估的风险因素？

主要发现

• 树状结构分层组LASSO模型在再入院预测中优于标准L1和组LASSO正则化方法，AUC达到约0.75。
• 术后感染和自杀意念在所有患者子群体中均被一致识别为最重要的诊断编码之一。
• 精神障碍和物质滥用被证实是再入院的重要预测因子，为长期存在的临床假设提供了实证支持。
• 社会经济因素如营养不良和缺乏稳定住房被识别为关键风险因素，表明社会决定因素与再入院之间存在强关联。
• 对于糖尿病或COPD等慢性病患者，合并症如短暂性脑缺血或胰腺疾病是再入院风险的强预测因子。
• 该模型揭示了某些诊断编码在特定慢性病子群体中具有独特预测力，凸显了共病在再入院风险中的重要性。