I-Corps: Data Completeness and Inconsistency Analysis Platform

I-Corps项目将影响医疗保健行业的一系列工作，提高财务效率，减少数据驱动的误诊和不当治疗。随着药物错误、误诊和其他可能对患者造成伤害并与医疗保健提供者产生责任问题的不准确情况的减少，医疗保健患者的结果和体验将得到改善。与获取电子健康数据相关的流程往往运行缓慢、不完整，而且往往没有得到患者的完全同意，也没有足够的元数据来跟踪治疗患者所需的关键信息。这里开发的解决方案可以潜在地实现适当的数据正确性，并解决不一致的问题，从而改善患者的治疗效果，并改善法律责任框架。这个I-Corps项目提供的智力优势将改善医疗保健信息行业。这包括分析数据完整性和一致性的数学模型和算法的综合。具体来说，这将导致一种混合机器学习方法，该方法将利用无监督学习来自动将各种电子健康记录分类为完整或不完整，并利用监督学习来确认这种分类，并进一步对输入数据准确和正确的可能性进行分级。此外，这也将导致强化学习应用的进化，以支持更高的准确性和异常检测。总体而言，该项目将提高医疗保健服务的质量，并在支持数据科学发展方面进行创新。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This I-Corps project will impact a range of healthcare industry jobs, increasing financial efficiency and reduce data-driven misdiagnosis and mistreatment. Healthcare patient outcomes and experiences will improve with reductions in medication errors, misdiagnosis, and other inaccuracies that can cause harm to patients and create liability issues with healthcare providers. Processes associated with acquiring electronic health data are often function slowly, incompletely, and often without the full consent of the patient or adequate metadata to track critical information required for treating a patient. The solution developed here can potentially enable proper data correctness and address inconsistencies which can lead to better patient outcomes and improve legal liability framework.This I-Corps project provides intellectual merits that will improve the healthcare information industry. This includes synthesis of mathematical models and algorithms that analyze data completeness and consistency. Specifically, this will lead to a hybrid machine learning approach that will utilize unsupervised learning to automatically classify various electronic health records as complete or incomplete, and supervised learning to confirm such classification and further grade the likelihood that the entered data is accurate and correct. Furthermore, this will also lead to evolutions in applications of reinforcement learning supporting greater accuracy, and anomaly detection. Overall, this project will lead to improving the quality of healthcare delivery and innovations in supporting evolutions in data science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.