Condition Monitoring for Evidence-based Care of Psychiatric Patient

该项目的目的是适应精神病人的循证护理状态监测技术。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得出的证据，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）， CBM主张使用各种传感器监控生产设备，以便实时诊断即将发生的故障，以便及时并根据需要采取正确的维护行动。将CBM技术应用于患者护理的挑战主要是由于患者数据中存在大量参数和差距（缺失值）。直接降维方法（与传统的维数转换技术相反）将用于识别和保留准确诊断模型构建所必需和充分的参数。为了填补数据空白，将开发一种无假设迭代插值技术。该技术将针对现有的imputation技术进行基准测试，并将制定指南，以根据数据集特征选择最佳技术。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规则整合医生的第一性原理知识和嵌入在数据中的知识。如果这项工作成功，将带来以下好处：(1)通过监测患者的病情，为临床医生提供优化药物使用的证据，从而将药物成本降至最低；(2)可以根据患者病情的变化及时调整治疗和用药，提高患者护理质量；(3)由于药物副作用得到监测和控制，患者的安全将得到最大限度的提高。从精神病人状况监测、诊断和预后中获得的经验教训将有助于全面实施循证病人护理。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集成的新知识，用于医疗保健应用中的诊断/预后建模。

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.