Condition Monitoring for Evidence-based Care of Psychiatric Patient

精神病患者循证护理的状态监测

• 批准号: 0555962
• 负责人: Samuel Huang
• 金额: --
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0555962&HistoricalAwards=false
• 关键词: Condition; Monitoring; Evidence; based; Care

The objective of this project is to adapt condition monitoring technology for evidence-based care of psychiatric patients. Evidence-based patient care promotes the collection, interpretation, and integration of applicable patient-reported, clinician-observed, and research-derived evidence to improve the quality of clinical judgments and facilitate cost-effective healthcare. This philosophy is similar to that of condition-based maintenance (CBM) in manufacturing, which advocates the monitoring of production equipment using various sensors to enable real-time diagnosis of impending failures so the right maintenance actions can take place in a timely fashion and on an as-needed basis. The challenge for adapting CBM technology to patient care is mainly due to the large number of parameters and gaps (missing values) in patient data. A direct dimensionality reduction approach (as opposed to traditional dimensionality transformation techniques) will be used to identify and retain parameters that are necessary and sufficient for accurate diagnosis model building. An assumption free iterative imputation technique will be developed to fill data gaps. This technique will be benchmarked against extant imputation techniques and guidelines will be developed for selecting the best technique based on dataset characteristics. Diagnosis/prognosis models will be constructed using a knowledge-based modeling approach, which is an expert centered data driven approach that uses IF-THEN rules to integrate physician first-principles knowledge and knowledge embedded in data.This work, if successful, will lead to the following benefits: (1) medication cost will be minimized because patient conditions are monitored to provide evidence for clinicians to optimize medication usage; (2) patient care quality will be improved because treatment and medication can be properly adjusted in a timely fashion based on changing patient conditions; and (3) patient safety will be maximized because medication side effects are monitored and can be kept in check. Lessons learned from psychiatric patient condition monitoring, diagnosis, and prognosis will shed lights on general implementation of evidence-based patient care. Specifically, the research will generate new knowledge regarding the integration of IF-THEN first-principles knowledge and data mining techniques for diagnosis/prognosis modeling in healthcare applications.

本计画的目的是将状态监测技术应用于精神病患的循证照护。 循证患者护理促进收集、解释和整合适用的患者报告、临床医生观察和研究来源的证据，以提高临床判断的质量，促进具有成本效益的医疗保健。 这种理念类似于制造业中的基于状态的维护（CBM），它提倡使用各种传感器来监控生产设备，以实现对即将发生的故障的实时诊断，从而可以及时并根据需要采取正确的维护措施。 使CBM技术适应患者护理的挑战主要是由于患者数据中的大量参数和差距（缺失值）。 直接降维方法（与传统的维度转换技术相反）将用于识别和保留精确诊断模型构建所需的参数。 将开发无假设迭代插补技术以填补数据空白。 这项技术将以现有的插补技术为基准，并将制定准则，根据数据集的特点选择最佳技术。 诊断/预后模型将使用基于知识的建模方法构建，这是一种以专家为中心的数据驱动方法，使用IF-THEN规则将医生的第一原理知识和数据中嵌入的知识整合在一起。这项工作如果成功，将带来以下好处：（1）由于可以监测患者的病情，为临床医生提供优化药物使用的证据，因此药物成本将最小化;（2）病人护理质素会得到改善，因为病人可根据病情的转变，适时地调整治疗和药物;及（3）病人的安全会得到最大程度的保障，因为药物的副作用会受到监察和控制。 从精神病患者病情监测、诊断和预后中吸取的经验教训将为循证患者护理的一般实施提供启示。 具体而言，该研究将产生关于IF-THEN第一原理知识和数据挖掘技术在医疗应用中的诊断/预后建模的整合的新知识。