SHB: Type I (EXP): Rehospitalization Analytics: Modeling and Reducing the Risks of Rehospitalization

SHB：I 类 (EXP)：再住院分析：建模和降低再住院风险

• 批准号: 1231742
• 负责人: Chandan Reddy
• 金额: $ 44.5万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1231742&HistoricalAwards=false
• 关键词: SHB; Type; EXP; Rehospitalization; Analytics

Hospitalizations account for more than 30% of the $2 trillion annual cost of healthcare in the United States. As many as 20% or more of all hospital admissions occur within 30 days of a previous discharge. Such rehospitalizations are not only expensive but are also potentially harmful, and most importantly, they are often preventable. Providing special care for a targeted group of patients who are at a high risk of rehospitalization can significantly improve the chances of avoiding rehospitalization. Estimating the predictive power of the clinical data collected during the hospitalization of a patient and effectively making predictions from such diverse patient records requires new analytical models. This project develops a 'rehospitalization analytics' framework that can identify, characterize and reduce the risks of rehospitalization for patients using a wide range of electronic health records. Specifically, the research objectives of this project are to develop: (i) integrated models that can effectively leverage multiple heterogeneous patient information sources and transfer the acquired knowledge about rehospitalization between different hospitals and patient groups in the presence of only few patient records, (ii) novel adaptable time-sensitive models that make predictions of the risk estimates in the presence of inherent concept drifts in the clinical data, and (iii) new regularization methods that can effectively extract the population-specific risk factors despite the presence of multiple correlations and grouped categorical clinical predictors. The methods are evaluated using heart failure patient records collected at the Henry Ford Health System in Detroit. The performance of the proposed models is compared against the state-of-the-art statistical and clinical tools that are currently applied for risk prediction.This project aims to provide a comprehensive, accurate, and timely assessment of risk of rehospitalizations, and has the potential to direct more aggressive treatments towards specific high-risk patients. Predictive models developed in this project could be widely adopted and have nation-wide impact because the source data is often available at the hospitals. This has the potential to improve the lives of patients, by reducing exacerbations, and reducing overall health care costs by reducing the number of hospitalizations. The computational models developed in this project could also be applied to other chronic diseases that have high rates of utilization and could benefit from improved targeting of intervention/resources. The educational objective of this project is to train the next generation of interdisciplinary researchers in the fields of data analytics and healthcare informatics. The progress of the project and the research findings are disseminated via the project website (http://www.cs.wayne.edu/~reddy/projects/health/).

在美国每年2万亿美元的医疗保健费用中，住院费用占30%以上。多达20%或更多的住院病例发生在上一次出院后的30天内。这种再住院治疗不仅昂贵，而且可能有害，最重要的是，它们通常是可以预防的。对再住院风险高的目标患者群体提供特殊护理可以显著提高避免再住院的机会。估计患者住院期间收集的临床数据的预测能力，并从如此多样化的患者记录中有效地进行预测，需要新的分析模型。该项目开发了一个“再住院分析”框架，该框架可以使用广泛的电子健康记录来识别、描述和降低患者再住院的风险。具体而言，该项目的研究目标是：(i)能够有效利用多个异质患者信息源的综合模型，并在仅存在少量患者记录的情况下在不同医院和患者群体之间传递所获得的关于再住院的知识；（ii）新颖的适应性时间敏感模型，在临床数据中存在固有概念漂移的情况下对风险估计进行预测；（iii）新的正则化方法，可以有效地提取人群特定的危险因素，尽管存在多重相关性和分组分类临床预测因子。这些方法是根据底特律亨利福特健康系统收集的心力衰竭患者记录进行评估的。所提出的模型的性能与目前应用于风险预测的最先进的统计和临床工具进行比较。该项目旨在提供全面、准确和及时的再住院风险评估，并有可能指导对特定高危患者进行更积极的治疗。在这个项目中开发的预测模型可以被广泛采用，并在全国范围内产生影响，因为源数据通常可以在医院获得。这有可能通过减少病情恶化来改善患者的生活，并通过减少住院次数来降低总体卫生保健成本。本项目开发的计算模型也可应用于使用率高的其他慢性病，并可从改进干预措施/资源的针对性中获益。该项目的教育目标是培养数据分析和医疗信息学领域的下一代跨学科研究人员。项目的进展和研究结果通过项目网站（http://www.cs.wayne.edu/~reddy/projects/health/）发布。

项目成果

Constrained elastic net based knowledge transfer for healthcare information exchange

用于医疗保健信息交换的基于约束弹性网络的知识转移

• DOI: 10.1007/s10618-014-0389-3
• 发表时间: 2015
• 期刊: Data Mining and Knowledge Discovery
• 影响因子: 4.8
• 作者: Li, Yan;Vinzamuri, Bhanukiran;Reddy, Chandan K.
• 通讯作者: Reddy, Chandan K.

CRISP: Consensus Regularized Selection based Prediction

• DOI: 10.1145/2983323.2983779
• 发表时间: 2016-10
• 期刊: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
• 作者: Ping Wang;Karthik K. Padthe;B. Vinzamuri;Chandan K. Reddy

Predicting Complications in Critical Care Using Heterogeneous Clinical Data

• DOI: 10.1109/access.2016.2618775
• 发表时间: 2016-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Huddar, Vijay;Desiraju, Bapu Koundinya;Reddy, Chandan K.
• 通讯作者: Reddy, Chandan K.