Long W J, Fraser H, Naimi S
MIT Lab for Computer Science, Cambridge, MA 02139, USA.
Artif Intell Med. 1997 May;10(1):5-24. doi: 10.1016/s0933-3657(97)00381-3.
Over the past dozen years, the Heart Disease Program (HDP) has been developed to assist physicians in reasoning about cardiovascular disorders. Driven by several evaluations, the inference mechanism has progressed from a logic based model, to a Bayesian Probability Network (BPN) and finally a pseudo-Bayesian network with temporal and severity reasoning. Though aspects of cardiovascular reasoning are handled well by BPNs, temporal reasoning, homeostatic feedback mechanisms and effects of disease severities require additional inference strategies. This article discusses how these reasoning problems are handled, and deals with closely linked issues in building the user interface to collect detailed cardiovascular data and provide clear explanations of diagnoses.
在过去的十二年里，心脏病项目（HDP）得以发展，旨在协助医生对心血管疾病进行推理。在多次评估的推动下，推理机制已从基于逻辑的模型发展到贝叶斯概率网络（BPN），最终发展为具有时间和严重程度推理的伪贝叶斯网络。虽然心血管推理的某些方面由贝叶斯概率网络处理得很好，但时间推理、稳态反馈机制和疾病严重程度的影响需要额外的推理策略。本文讨论了如何处理这些推理问题，并处理了在构建用户界面以收集详细的心血管数据并提供清晰的诊断解释方面密切相关的问题。
Zotero 插件