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。

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.