标题： 基于合并ICD-10代码的住院治疗正则化对数线性成本模型 显示英文标题

标题： Regularizing Log-Linear Cost Models for Inpatient Stays by Merging ICD-10 Codes

摘要： 医疗保健研究中的成本模型必须平衡可解释性、准确性和参数一致性。 然而，可解释的模型往往难以同时实现准确性和一致性。 对于高维回归的普通最小二乘法（OLS）模型可以准确，但在使用高度细粒度的ICD-10诊断代码作为预测变量时，无法在时间上产生稳定的回归系数。 这种不稳定性是因为许多ICD-10代码在医疗数据集中出现频率较低。 虽然正则化方法如岭回归可以解决这个问题，但它们可能会丢弃重要的预测变量。 在这里，我们证明在OLS中减少ICD-10代码的粒度是一种有效的正则化策略，同时保留所有诊断代码类别的表示。 通过将ICD-10代码从七个字符（例如，T67.0XXA，T67.0XXD）截断为六个（例如，T67.0XX）或更少，我们降低了回归问题的维度，同时保持了模型的可解释性和一致性。 数学上，OLS中预测变量的合并会导致海森矩阵的迹增加，从而降低系数估计的方差。 我们的发现解释了为什么在现实世界的风险调整和成本模型中，更广泛的诊断分组如DRGs和HCC代码比高度细粒度的ICD-10代码更受青睐。 显示英文摘要

摘要： Cost models in healthcare research must balance interpretability, accuracy, and parameter consistency. However, interpretable models often struggle to achieve both accuracy and consistency. Ordinary least squares (OLS) models for high-dimensional regression can be accurate but fail to produce stable regression coefficients over time when using highly granular ICD-10 diagnostic codes as predictors. This instability arises because many ICD-10 codes are infrequent in healthcare datasets. While regularization methods such as Ridge can address this issue, they risk discarding important predictors. Here, we demonstrate that reducing the granularity of ICD-10 codes is an effective regularization strategy within OLS while preserving the representation of all diagnostic code categories. By truncating ICD-10 codes from seven characters (e.g., T67.0XXA, T67.0XXD) to six (e.g., T67.0XX) or fewer, we reduce the dimensionality of the regression problem while maintaining model interpretability and consistency. Mathematically, the merging of predictors in OLS leads to increased trace of the Hessian matrix, which reduces the variance of coefficient estimation. Our findings explain why broader diagnostic groupings like DRGs and HCC codes are favored over highly granular ICD-10 codes in real-world risk adjustment and cost models.