Abstract：
OBJECTIVE: Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification.
METHODS: The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients.
RESULTS: Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands.
CONCLUSIONS: According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition.
CONCLUSIONS: Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.
METHODS: The proposed technique applies an adapted line search across all potential hyperparameter values. Additionally, DeLong test is integrated to ensure the selected value produces an accuracy insignificantly different from the best achievable risk score accuracy. We assessed the approach through two case studies predicting diabetic retinopathy (DR) within six months and hip fracture readmissions (HFR) within 30 days, involving cohorts of 90 400 diabetic patients and 18 065 hip fracture patients.
RESULTS: Our scores achieve accuracies insignificantly different from those obtained by existing approaches, reaching AUROCs of 0.803 and 0.645 for DR and HFR predictions, respectively. Regarding the scale, our scores ranged 0-53 for DR and 0-15 for HFR, while scores produced by existing methods frequently spanned hundreds or thousands.
CONCLUSIONS: According to the assessment, our risk scores offer simple and accurate predictions for diseases. Furthermore, our new DR score provides a competitive alternative to state-of-the-art risk scores for DR, while our HFR case study presents the first risk score for this condition.
CONCLUSIONS: Our technique offers a generalizable framework for crafting precise risk scores of compact scales, addressing the demand for user-friendly and effective risk stratification tool in healthcare.
摘要：
目的：开发一种新技术来确定与单个风险点相对应的最佳回归单元数量，同时从基于逻辑回归的疾病预测模型创建风险评分系统。这个超参数的最佳值平衡了简单性和准确性，为患者风险分层提供小规模和高准确性的风险评分。
方法：所提出的技术在所有潜在的超参数值上应用自适应线搜索。此外,集成了DeLong测试，以确保选定的值产生的准确性与最佳可实现的风险评分准确性没有显着差异。我们通过两个病例研究评估方法，预测糖尿病视网膜病变（DR）在6个月内和髋部骨折再入院（HFR）在30天内，涉及90400名糖尿病患者和18065名髋部骨折患者。
结果：我们的分数与现有方法获得的分数没有显着差异，DR和HFR预测的AUROC达到0.803和0.645，分别。关于规模,我们的DR评分为0-53，HFR评分为0-15，而现有方法产生的分数经常跨越数百或数千。
结论：根据评估，我们的风险评分为疾病提供了简单而准确的预测.此外，我们的新DR评分为DR的最新风险评分提供了一个有竞争力的替代方案，而我们的HFR病例研究显示了这种情况的第一个风险评分。
结论：我们的技术为制作紧凑量表的精确风险评分提供了一个可概括的框架，解决医疗保健中对用户友好和有效的风险分层工具的需求。
方法：所提出的技术在所有潜在的超参数值上应用自适应线搜索。此外,集成了DeLong测试，以确保选定的值产生的准确性与最佳可实现的风险评分准确性没有显着差异。我们通过两个病例研究评估方法，预测糖尿病视网膜病变（DR）在6个月内和髋部骨折再入院（HFR）在30天内，涉及90400名糖尿病患者和18065名髋部骨折患者。
结果：我们的分数与现有方法获得的分数没有显着差异，DR和HFR预测的AUROC达到0.803和0.645，分别。关于规模,我们的DR评分为0-53，HFR评分为0-15，而现有方法产生的分数经常跨越数百或数千。
结论：根据评估，我们的风险评分为疾病提供了简单而准确的预测.此外，我们的新DR评分为DR的最新风险评分提供了一个有竞争力的替代方案，而我们的HFR病例研究显示了这种情况的第一个风险评分。
结论：我们的技术为制作紧凑量表的精确风险评分提供了一个可概括的框架，解决医疗保健中对用户友好和有效的风险分层工具的需求。
