并发症的粗糙集与推理——医学推理中的颗粒计算

S. Tsumoto
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引用次数: 0

摘要

医学推理建模中最困难的问题之一是对并发症的诊断过程进行建模。在医学背景下,病人有时患有多种疾病,症状复杂,这使得鉴别诊断非常困难。例如,在头痛领域,患有偏头痛(一种血管疾病)的患者也可能患有肌肉收缩性头痛(一种肌肉疾病)。在这种情况下,血管疾病特有的症状将与肌肉疾病特有的症状一起观察。由于头痛诊断的基本过程之一是区分血管疾病和肌肉疾病,简单的规则将无法排除这两种疾病中的一种。然而,医学专家没有这个问题,并得出这两种疾病的结论。本文介绍了三种复杂性推理模型,并利用表征和粗糙集模型对其进行建模。这种清晰的表述表明,该模型应该被医学专家含蓄地使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rough sets and reasoning about complications-granular computation in medical reasoning
One of the most difficult problems in modeling medical reasoning is to model a procedure for diagnosis about complications. In medical contexts, a patient sometimes suffers from several diseases and has complicated symptoms, which makes a differential diagnosis very difficult. For example, in the domain of headache, a patient suffering from migraine, (a vascular disease), may also suffer from muscle contraction headache (a muscular disease). In this case, symptoms specific to vascular diseases will be observed with those specific to muscular ones. Since one of the essential processes in diagnosis of headache is discrimination between vascular and muscular diseases, simple rules will not work to rule out one of the two groups. However, medical experts do not have this problem and conclude both diseases. In this paper, three models for reasoning about complications are introduced and modeled by using characterization and rough set model. This clear representation, suggests that this model should be used by medical experts implicitly.
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