Loftus Tyler J, Upchurch Gilbert R, Delitto Daniel, Rashidi Parisa, Bihorac Azra
Department of Surgery, University of Florida Health, Gainesville, FL, United States.
Departments of Biomedical Engineering, Computer and Information Science and Engineering, and Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.
Front Artif Intell. 2020 Jan;2. doi: 10.3389/frai.2019.00032. Epub 2020 Jan 21.
Life is filled with puzzles and mysteries, and we often fail to recognize the difference. As described by Gregory Treverton and Malcolm Gladwell, puzzles are solved by gathering and assimilating all relevant data in a logical, linear fashion, as in deciding which antibiotic to prescribe for an infection. In contrast, mysteries remain unsolved until all relevant data are analyzed and interpreted in a way that appreciates their depth and complexity, as in determining how to best modulate the host immune response to infection. When investigating mysteries, we often fail to appreciate their depth and complexity. Instead, we gather and assimilate more data, treating the mystery like a puzzle. This strategy is often unsuccessful. Traditional approaches to predictive analytics and phenotyping in surgery use this strategy.
生活充满了谜题和奥秘,而我们常常意识不到它们的区别。正如格雷戈里·特里弗顿和马尔科姆·格拉德威尔所描述的,谜题通过以逻辑、线性的方式收集和整合所有相关数据来解决,比如决定为感染开哪种抗生素。相比之下,奥秘在所有相关数据以一种能认识到其深度和复杂性的方式进行分析和解读之前都是无法解决的,比如确定如何最好地调节宿主对感染的免疫反应。在探究奥秘时,我们常常无法认识到它们的深度和复杂性。相反,我们收集和整合更多数据,把奥秘当作谜题来处理。这种策略往往并不成功。手术中传统的预测分析和表型分析方法就采用了这种策略。