分类同频分析：聚类诊断代码预测再入院
Categorical Co-Frequency Analysis: Clustering Diagnosis Codes to Predict Hospital Readmissions
摘要：
准确预测患者30天再入院的风险将使医院能够有效地分配资源密集型干预措施。根据协变量与再入院风险之间关系的相似性，提出了一种新的分类共频分析（CoFA）方法，用于对国际疾病分类（ICD）中的诊断代码进行聚类。CoFA通过两个诊断在同一方向上分裂与在随机森林中分裂的频率来测量诊断之间的相似性，以预测再入院风险。将CoFA应用于伯克希尔医疗中心的去识别数据，我们确定了三组再入院风险不同的诊断。为了评估CoFA，我们将使用ICD专业和CoFA组的再入院风险模型与无诊断变量的基线模型进行了比较。我们发现用ICD专业取代CoFA识别的聚类简化了模型，而不会影响预测的准确性。为每个ICD主要和CoFA组拟合单独的模型并没有改善预测，这表明再入院风险在诊断组之间可能更加同质而非异质。
英文摘要：
Accurately predicting patients' risk of 30-day hospital readmission would enable hospitals to efficiently allocate resource-intensive interventions. We develop a new method, Categorical Co-Frequency Analysis (CoFA), for clustering diagnosis codes from the International Classification of Diseases (ICD) according to the similarity in relationships between covariates and readmission risk. CoFA measures the similarity between diagnoses by the frequency with which two diagnoses are split in the same direction versus split apart in random forests to predict readmission risk. Applying CoFA to de-identified data from Berkshire Medical Center, we identified three groups of diagnoses that vary in readmission risk. To evaluate CoFA, we compared readmission risk models using ICD majors and CoFA groups to a baseline model without diagnosis variables. We found substituting ICD majors for the CoFA-identified clusters simplified the model without compromising the accuracy of predictions. Fitting separate models for each ICD major and CoFA group did not improve predictions, suggesting that readmission risk may be more homogeneous that heterogeneous across diagnosis groups.
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