Doan A, Haddawy P, Kahn C E
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, USA.
Proc Annu Symp Comput Appl Med Care. 1995:299-303.
Clinical decision analysis seeks to identify the optimal management strategy by modelling the uncertainty and risks entailed in the diagnosis, natural history, and treatment of a particular problem or disorder. Decision trees are the most frequently used model in clinical decision analysis, but can be tedious to construct, cumbersome to use, and computationally prohibitive, especially with large, complex decision problems. We present a new method for clinical decision analysis that combines the techniques of decision theory and artificial intelligence. Our model uses a modular representation of knowledge that simplifies model building and enables more fully automated decision making. Moreover, the model exploits problem structures to yield better computational efficiency. As an example we apply our techniques to the problem of management of acute deep venous thrombosis.
临床决策分析旨在通过对特定问题或疾病的诊断、自然病史及治疗过程中所涉及的不确定性和风险进行建模，来确定最佳管理策略。决策树是临床决策分析中最常用的模型，但构建起来可能很繁琐，使用起来很麻烦，并且计算量很大，尤其是对于大型复杂的决策问题。我们提出了一种结合决策理论和人工智能技术的临床决策分析新方法。我们的模型采用模块化知识表示，简化了模型构建并实现了更完全自动化的决策。此外，该模型利用问题结构来提高计算效率。作为一个例子，我们将我们的技术应用于急性深静脉血栓形成的管理问题。
