Grossi Enzo
Medical Department, Bracco SpA Milan, Italy.
BMC Cardiovasc Disord. 2006 May 3;6:20. doi: 10.1186/1471-2261-6-20.
In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years
The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed.
The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level.
近年来，医学界提出了多种心血管风险评估算法。这些算法考虑了多个变量，并将结果表示为未来10至20年内发生重大致命或非致命心血管事件的风险百分比。
作者确定了这些算法的三个主要缺陷，这些缺陷与经典统计方法在处理这类非线性和复杂信息时的局限性有关。这些缺陷包括无法捕捉疾病的复杂性、无法捕捉过程动态以及个体风险评估的宽泛置信区间。人工智能工具在试图克服这些局限性方面可能具有优势。本文对与人工神经网络和模糊逻辑相关的理论背景及一些应用实例进行了回顾和讨论。
目前，预测算法在评估个体未来心血管事件绝对风险时受到方法和数学缺陷的阻碍。与人工智能相关的更新方法，如模糊逻辑和人工神经网络，似乎能更好地应对因易感因素、心血管事件发生数据以及个体层面未来事件预测之间的相关性而导致的日益增加的复杂性挑战。
