RT期刊文章SR电子T1肺动脉高血压风险分层使用贝叶斯分析JF欧洲呼吸杂志Jo EUR Respir J FD欧洲呼吸学会SP 2000008 Do 10.1183 / 13993003.00008-2020 VO 56是2 A1 Kanwar，Manreet K. A1 Gomberg-Maitland，188bet官网地址Mardi A1 Hoper，Marius A1 Pausch，Christine A1 Pittrow，David A1奇怪，James J. A1 Zhao，Carol A1 Scott，Jacqueline V.A1 Druzdzel，Marek J.A1 Kraisangka，Jidapa A1 Lohmueller，Lisa A1 Antaki，詹姆斯A1 Benza，Raymond L. YR 2020 UL //www.qdcxjkg.com/content/56/2/2000008.Abstract ab背景电流风险分层工具肺动脉高压（PAH）的歧视性能力受到限制，部分地由于假设预后临床变量具有与临床结果的独立和线性关系。我们试图展示贝叶斯网络的机器学习在提高现有最先进的风险分层工具的预测能力方面的效用，揭示了2.0.9.40.方法我们派生了一个树增强的天真贝叶斯模型（标题为Phora）预测揭示登记处包括的PAH患者的1年生存率，使用相同的变量和揭示2.0发现的剪切点。Phora模型在内部（在显示的注册表中）和外部（在Compera和Phsanz注册表中）进行验证。基于2015年欧洲心脏病学/欧洲呼吸社会指南，患者分为低，中药和高风险（分别为<5％，5％，5-20％和> 10％12个月死亡率。结果Phora188bet官网地址用于预测1年生存率的0.80的曲线（AUC）下的区域，这是揭示2.0（AUC 0.76）的改善。当在Compera和Phsanz注册管理机构中验证时，Phora分别显示了0.74和0.80的AUC。 1-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (p<0.001), with excellent separation between low-, intermediate- and high-risk groups in all three registries.Conclusion Our Bayesian network-derived risk prediction model, PHORA, demonstrated an improvement in discrimination over existing models. This is reflective of the ability of Bayesian network-based models to account for the interrelationships between clinical variables on outcome, and tolerance to missing data elements when calculating predictions.Bayesian machine-learning algorithms can improve discrimination of risk stratification in PAH. Our BN model, titled PHORA, predicts 1-year mortality with an AUC of 0.8, risk-stratifies patients effectively and is validated in two independent PAH registries. https://bit.ly/2xc0EVJ