Methods for Development of Optimized Complex Expert Systems

优化复杂专家系统的开发方法

• 批准号: RGPIN-2014-04486
• 负责人: Seto, Emily
• 金额: $ 1.6万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=664776
• 关键词: Methods; Development; Optimized; Complex; Expert

Expert systems (ie, computer systems that emulate the decision-making ability of a human expert) can support decision-making in numerous fields including medicine, finance, and aviation. In our digital age, more data is being collected than ever before. This is particularly true in healthcare for both clinicians and patients who try to manage complex (multiple) chronic conditions. New sources of health data, including continuous physiological measurements taken with home medical devices can now be factored in for decision-making. In addition, computerized access to lab results and medication lists can now be leveraged for use in expert systems for both patients and clinicians. While all these data may be relevant, people have the ability to assimilate only a limited amount of information for decision-making.**Reported studies have indicated that expert systems supporting clinical decision-making can improve the performance of healthcare providers especially for purposes of diagnosing, but research on their use with patients are few. In addition, the processes to develop expert systems that incorporate varied and continuous sources of patient data, such as physiological data from the patient's home, have not been well studied. This research is timely not only because of the proliferation of novel and large data sources, but also because of the recent awareness of the significant impact of poor self-care and suboptimal clinical management of people with multiple chronic conditions (5% of the population consumes > 50% of all dollars devoted to healthcare). However, expert systems used in healthcare must meet an especially high level of rigor in terms of ensuring safety, due to the potential serious negative consequences of inappropriate recommendations from the expert systems.**This Discovery Grant would support research investigating and applying expert systems for complex decision-making that incorporates emerging sources of data. This will include investigation into: 1) methods to program and visualize complex expert systems 2) the applicability of different types of logic to be used in expert systems, 3) the safety and utility of embedding user preferences into expert systems, and 4) the role and methods to integrate novel data sources into expert systems for chronic disease management. In particular, this Discovery Grant will fund graduate students to research three specific projects:1) investigate the processes to develop optimized patient expert systems, 2) investigate the processes to develop optimized clinician expert systems, and 3) determine how to incorporate patient health record (PHR) data into expert systems. **Many of the processes, techniques, and insights could be applied to other fields. For example, expert systems could be developed that incorporate the growing available data on an individual's spending, savings, and investments to support their financial decision-making. Future planned research includes investigation into increasingly complex healthcare expert systems (ie, incorporating additional sources of data such as genomics) and other areas of artificial intelligence (eg, predictions of health outcomes or acute events). A process to create optimized expert systems can help manage the "big data challenge" in many sectors.

专家系统(即模拟人类专家决策能力的计算机系统)可以支持包括医学、金融和航空在内的许多领域的决策。在我们的数字时代，收集的数据比以往任何时候都多。对于试图管理复杂(多种)慢性疾病的临床医生和患者来说，这一点在医疗保健中尤其如此。健康数据的新来源，包括用家用医疗设备进行的连续生理测量，现在可以作为决策的因素。此外，现在可以利用计算机获取实验室结果和药物清单，供患者和临床医生在专家系统中使用。虽然所有这些数据都可能是相关的，但人们只有能力吸收有限的信息来进行决策。**报道的研究表明，支持临床决策的专家系统可以改善医疗保健提供者的绩效，特别是在诊断方面，但关于它们在患者中的使用的研究很少。此外，开发包含不同和连续的患者数据来源的专家系统的过程，例如来自患者家中的生理数据，还没有得到很好的研究。这项研究之所以及时，不仅是因为新的大型数据源的激增，还因为最近人们意识到，对患有多种慢性病的人来说，糟糕的自我护理和次优的临床管理会产生重大影响(5%的人口花费在医疗保健上的资金占所有资金的50%)。然而，医疗保健中使用的专家系统在确保安全性方面必须达到特别高的严格水平，因为来自专家系统的不适当建议可能会产生严重的负面后果。**这项发现拨款将支持调查和应用专家系统进行复杂决策的研究，这些决策纳入了新兴的数据源。这将包括调查：1)对复杂专家系统进行编程和可视化的方法；2)在专家系统中使用不同类型的逻辑的适用性；3)将用户偏好嵌入到专家系统中的安全性和实用性；以及4)将新的数据源集成到慢性病管理专家系统中的作用和方法。特别是，这笔发现助学金将资助研究生研究三个具体项目：1)调查开发优化患者专家系统的过程，2)调查开发优化临床医生专家系统的过程，以及3)确定如何将患者健康记录(PHR)数据纳入专家系统。**许多流程、技术和见解可以应用于其他领域。例如，可以开发专家系统，将有关个人支出、储蓄和投资的日益增长的可用数据整合在一起，以支持他们的财务决策。未来计划的研究包括调查日益复杂的医疗专家系统(例如，纳入其他数据来源，如基因组学)和其他人工智能领域(例如，对健康结果或急性事件的预测)。创建优化专家系统的过程可以帮助管理许多行业的“大数据挑战”。