Personalized Decision Support Driven by Similarity Metrics

决策是各个领域的一个重要方面，包括医学、公共卫生、政治、经济、商业、零售和体育。虽然需要基于定量证据的最佳决策，但由于缺乏科学研究支持的既定决策准则，因此会产生挑战。如果没有明确的指导方针，决策者就会求助于以前的培训、当地文化和轶事经验，这往往会导致有偏见的推理。由于信息技术的进步，今天电子数据的大量生产和存储为一个有吸引力的替代方案打开了大门，即数据驱动的决策，它从有价值的电子数据中提取相关知识，并立即将其提供给决策者。**我建议研究的数据驱动决策的一个有前途的途径是基于对电子数据中类似案例的分析的个性化决策支持。设想的核心技术是相似度量（SMs）的计算，该度量客观地量化两种情况彼此相似的程度。根据应用程序的不同，“案例”可以是患者、组织、消费者或社区。基于SM，个性化决策支持范式首先识别与正在调查的当前案例相似的过去案例。通过分析这些相似案例在等效时间点之后的情况，为个性化决策提供信息是可行的。具体来说，SMs可以帮助预测当前案例的未来，或者通过调查过去决策的影响来促进决策分析。本研究假设纵向模式（即作为时间函数的数据）除了静态信息外，在SMs中也起着至关重要的作用，因为它们捕获了动态特征。现在我们有了强大的计算机，廉价的数据存储，以及由于互联网和无处不在的移动设备而产生的新颖数据源，相似性的概念可以被收紧，从传统的一刀切决策转向量身定制的实时决策。毕竟，每个案例在某些细节层面上都是独一无二的。**本研究的主要成果将是一个完整的决策支持基础设施，包括SMs、数据仓库、计算节点和具有有效用户界面和数据可视化的软件决策支持工具。将利用公共数据库，以避免耗时的数据收集。数学和统计建模以及人工智能将被用于开发通用和特定问题的SMs，而高性能计算将成为决策支持工具的支柱。SMs与个性化决策支持以及与决策相关的知识实时培养的无缝集成是完全新颖的，将推动当前最先进的决策支持过程的发展。**这个拟议的研究项目是数据科学的核心，预计在可预见的未来，它将在学术界和工业界创造重要的就业机会。然而，目前大多数大学都没有开设数据科学的学位课程，因为它是一个相对较新的领域，是计算机科学、统计学和数学等几个不同领域的交叉点。通过让本科生和研究生领导研究的各个方面，拟议研究的首要任务是培养高素质的人才，使其成为熟练的数据科学家，既具备动手计算技能，又具备理论基础。

Decision making is an important aspect of various fields including medicine, public health, politics, economics, business, retail, and sports. Although optimal decisions rooted in quantitative evidence are desired, challenges arise in the absence of established decision making guidelines supported by scientific research. When there is no clear guideline, decision makers resort to previous training, local culture, and anecdotal experiences, which frequently lead to biased reasoning. Today's massive production and storage of electronic data thanks to advances in information technology open doors to an attractive alternative, namely data-driven decision making, which extracts relevant knowledge from valuable electronic data and instantaneously delivers it to decision makers. **One promising avenue in data-driven decision making that I propose to investigate is personalized decision support based on analysis of similar cases in electronic data. The envisioned core technology is computation of similarity metrics (SMs) that objectively quantify the extent to which two cases are similar to each other. Depending on the application, a `case' can be a patient, organization, consumer, or community. Based on an SM, the personalized decision support paradigm first identifies past cases that are just like a current case under investigation. By analyzing what happened to those similar cases after their equivalent point in time, it is feasible to inform personalized decision making. Specifically, SMs can help forecast the future of the present case or facilitate decision analysis via investigating the effects of past decisions. This proposed research hypothesizes that longitudinal patterns (i.e., data as a function of time), in addition to just static information, play a crucial role in SMs since they capture dynamic characteristics. Now that we have powerful computers, inexpensive data storage, and novel data sources thanks to the Internet and ubiquitous mobile devices, the concept of similarity can be tightened to move away from traditional one-size-fits-all decisions to tailor-made real-time decisions. After all, every case is unique at some level of detail.**The primary outcome of this proposed research will be a complete decision support infrastructure encompassing SMs, data warehouses, computational nodes, and software decision support tools with effective user interfaces and data visualization. Public databases will be utilized in order to bypass time-consuming data collection. Mathematical and statistical modeling as well as artificial intelligence will be leveraged to develop both generic and problem-specific SMs, while high-performance computing will be the backbone of the decision support tools. The seamless integration of SMs with personalized decision support and real-time cultivation of knowledge relevant to decision making are completely novel and will push the envelope of the current state-of-the-art decision support processes.**This proposed research program is at the core of data science, which is expected to be a significant job creator in the foreseeable future in both academia and industry. However, most universities currently do not operate degree programs in data science because it is a relatively new field in the intersection of several distinct fields including computer science, statistics, and mathematics. By having undergraduate and graduate students lead all aspects of the research, a top priority in the proposed research is to train highly qualified personnel as proficient data scientists equipped with both hands-on computing skills and theoretical foundations.