Abstract：
UNASSIGNED: Von Willebrand disease (VWD) is underdiagnosed, often delaying treatment. VWD claims coding is limited and includes no severity qualifiers; improved identification methods for VWD are needed. The aim of this study is to identify and characterize undiagnosed symptomatic persons with VWD in the US from medical insurance claims using predictive machine learning (ML) models.
UNASSIGNED: Diagnosed and potentially undiagnosed VWD cohorts were defined using Komodo longitudinal US claims data (January 2015-March 2020). ML models were built using key characteristics predictive of VWD diagnosis from the diagnosed cohort. Two ML models predicted VWD diagnosis with the highest accuracy in females (random forest; 84%) and males (gradient boosting machine; 85%). Undiagnosed persons suspected to have VWD were identified using an 80% cutoff probability; profiles of key characteristics were constructed.
UNASSIGNED: The trained ML models were applied to the undiagnosed cohort (28,463 females; 20,439 males) with suspected VWD. Fifty-two percent of undiagnosed females had heavy menstrual bleeding, a key pre-diagnosis symptom. Undiagnosed males tended to have more frequent medical procedures, hospitalizations, and emergency room visits compared with undiagnosed females.
UNASSIGNED: ML algorithms successfully identified potentially undiagnosed symptomatic people with VWD, although many may remain undiagnosed and undertreated. External validation of the algorithms is recommended.
UNASSIGNED: Diagnosed and potentially undiagnosed VWD cohorts were defined using Komodo longitudinal US claims data (January 2015-March 2020). ML models were built using key characteristics predictive of VWD diagnosis from the diagnosed cohort. Two ML models predicted VWD diagnosis with the highest accuracy in females (random forest; 84%) and males (gradient boosting machine; 85%). Undiagnosed persons suspected to have VWD were identified using an 80% cutoff probability; profiles of key characteristics were constructed.
UNASSIGNED: The trained ML models were applied to the undiagnosed cohort (28,463 females; 20,439 males) with suspected VWD. Fifty-two percent of undiagnosed females had heavy menstrual bleeding, a key pre-diagnosis symptom. Undiagnosed males tended to have more frequent medical procedures, hospitalizations, and emergency room visits compared with undiagnosed females.
UNASSIGNED: ML algorithms successfully identified potentially undiagnosed symptomatic people with VWD, although many may remain undiagnosed and undertreated. External validation of the algorithms is recommended.
摘要：
■血管性血友病（VWD）未被诊断，经常拖延治疗。VWD索赔编码是有限的，不包括严重性限定符；需要改进的VWD识别方法。这项研究的目的是：使用医疗保险索赔来开发预测性机器学习（ML）模型，在美国识别和表征未诊断的VWD患者。
■使用科莫多纵向USclaims数据（2015年1月至2020年3月）定义诊断和潜在未诊断的VWD队列。ML模型是使用从诊断队列中预测VWD诊断的关键特征建立的。两个ML模型预测女性（随机森林；84％）和男性（梯度增强机；85％）的VWD诊断准确率最高。使用80％的截止概率识别怀疑患有VWD的未诊断人员；构建了关键特征的概要。
■将经过训练的ML模型应用于疑似VWD的未诊断队列（28,463名女性；20439名男性）。52%未确诊的女性有大量月经出血，明确诊断前的症状。未确诊的男性往往有更频繁的医疗程序，住院治疗,与未确诊的女性相比，急诊室就诊。
■ML算法成功识别出潜在未诊断的VWD患者，尽管许多人可能仍未被诊断和治疗不足。建议对算法进行外部验证。
■使用科莫多纵向USclaims数据（2015年1月至2020年3月）定义诊断和潜在未诊断的VWD队列。ML模型是使用从诊断队列中预测VWD诊断的关键特征建立的。两个ML模型预测女性（随机森林；84％）和男性（梯度增强机；85％）的VWD诊断准确率最高。使用80％的截止概率识别怀疑患有VWD的未诊断人员；构建了关键特征的概要。
■将经过训练的ML模型应用于疑似VWD的未诊断队列（28,463名女性；20439名男性）。52%未确诊的女性有大量月经出血，明确诊断前的症状。未确诊的男性往往有更频繁的医疗程序，住院治疗,与未确诊的女性相比，急诊室就诊。
■ML算法成功识别出潜在未诊断的VWD患者，尽管许多人可能仍未被诊断和治疗不足。建议对算法进行外部验证。
