• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Frontiers in Public Health >The CARPEDIEM Algorithm: A Rule-Based System for Identifying Heart Failure Phenotype with a Precision Public Health Approach
【24h】

The CARPEDIEM Algorithm: A Rule-Based System for Identifying Heart Failure Phenotype with a Precision Public Health Approach

机译：CARPEDIEM算法：一种基于规则的系统，通过精确的公共卫生方法识别心力衰竭表型

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Modern medicine remains dependent on the accurate evaluation of a patient’s health state, recognizing that disease is a process that evolves over time and interacts with many factors unique to that patient. The CARPEDIEM project represents a concrete attempt to address these issues by developing reproducible algorithms to support the accuracy in detection of complex diseases. This study aims to establish and validate the CARPEDIEM approach and algorithm for identifying those patients presenting with or at risk of heart failure (HF) by studying 153,393 subjects in Italy, based on patient information flow databases and is not reliant on the electronic health record to accomplish its goals. The resulting algorithm has been validated in a two-stage process, comparing predicted results with (1) HF diagnosis as identified by general practitioners (GPs) among the reference cohort and (2) HF diagnosis as identified by cardiologists within a randomly sampled subpopulation of 389 patients. The sources of data used to detect HF cases are numerous and were standardized for this study. The accuracy and the predictive values of the algorithm with respect to the GPs and the clinical standards are highly consistent with those from previous studies. In particular, the algorithm is more efficient in detecting the more severe cases of HF according to the GPs’ validation (specificity increases according to the number of comorbidities) and external validation (NYHA: II–IV; HF severity index: 2, 3). Positive and negative predictive values reveal that the CARPEDIEM algorithm is most consistent with clinical evaluation performed in the specialist setting, while it presents a greater ability to rule out false-negative HF cases within the GP practice, probably as a consequence of the different HF prevalence in the two different care settings. Further development includes analyzing the clinical features of false-positive and -negative predictions, to explore the natural clustering of markers of chronic conditions by adding additional methodologies, e.g., Social Network Analysis. CARPEDIEM establishes the potential that an algorithmic approach, based on integrating administrative data with other public data sources, can enable the development of low cost and high value population-based evaluations for improving public health and impacting public health policies.
机译：现代医学仍然依赖于对患者健康状况的准确评估，认识到疾病是一个随时间演变的过程，并与该患者独特的许多因素相互作用。 CARPEDIEM项目是通过开发可再现的算法以支持复杂疾病检测的准确性来解决这些问题的具体尝试。这项研究旨在通过基于患者信息流数据库研究意大利的153393名受试者，并且不依赖于电子健康记录来建立和验证CARPEDIEM方法和算法，以识别出现心力衰竭或有心力衰竭（HF）的患者。完成其目标。所产生的算法已通过两步验证，将预测结果与（1）参考人群中全科医生（GP）鉴定的HF诊断和（2）心脏病专家鉴定的随机抽样亚群中的HF诊断进行了比较。 389名患者。用于检测心力衰竭病例的数据来源众多，并已标准化为该研究。关于GP和临床标准的算法的准确性和预测值与以前的研究高度一致。特别是，根据GP的验证（特异性随合并症的数量而增加）和外部验证（NYHA：II–IV; HF严重程度指数：2、3），该算法在检测更严重的HF病例方面更为有效。 。正预测值和负预测值表明，CARPEDIEM算法与专家环境中的临床评估最一致，而在全科医生实践中，它具有更大的排除假阴性心衰病例的能力，这可能是不同心衰患病率的结果在两个不同的护理设置中。进一步的发展包括分析假阳性和阴性预测的临床特征，通过添加其他方法，例如社交网络分析，探索慢性病标志物的自然聚类。 CARPEDIEM确立了一种潜在的可能性，即基于将行政数据与其他公共数据源集成在一起的算法方法，可以开发低成本和高价值的基于人群的评估方法，以改善公共卫生并影响公共卫生政策。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号