School of Computer Science and Cybersecurity, Communication University of China, Chaoyang District, Beijing 100024, China.
Department of Computer Science and Technology, Tsinghua University, Haidian District, Beijing 100084, China.
Comput Math Methods Med. 2020 Jan 24;2020:1541989. doi: 10.1155/2020/1541989. eCollection 2020.
The accurate differentiation of the subtypes of benign paroxysmal positional vertigo (BPPV) can significantly improve the efficacy of repositioning maneuver in its treatment and thus reduce unnecessary clinical tests and inappropriate medications. In this study, attempts have been made towards developing approaches of causality modeling and diagnostic reasoning about the uncertainties that can arise from medical information. A dynamic uncertain causality graph-based differential diagnosis model for BPPV including 354 variables and 885 causality arcs is constructed. New algorithms are also proposed for differential diagnosis through logical and probabilistic inference, with an emphasis on solving the problems of intricate and confounding disease factors, incomplete clinical observations, and insufficient sample data. This study further uses vertigo cases to test the performance of the proposed method in clinical practice. The results point to high accuracy, a satisfactory discriminatory ability for BPPV, and favorable robustness regarding incomplete medical information. The underlying pathological mechanisms and causality semantics are verified using compact graphical representation and reasoning process, which enhance the interpretability of the diagnosis conclusions.
准确区分良性阵发性位置性眩晕（BPPV）的亚型可以显著提高复位手法治疗的疗效，从而减少不必要的临床检查和不当的药物治疗。在这项研究中，我们尝试开发因果建模方法和诊断推理方法，以处理医学信息中可能出现的不确定性。构建了一个包含 354 个变量和 885 个因果弧的基于动态不确定因果图的 BPPV 鉴别诊断模型。还提出了新的基于逻辑和概率推理的鉴别诊断算法，重点解决疾病因素复杂、混淆、临床观察不完整和样本数据不足的问题。本研究进一步使用眩晕病例来测试所提出的方法在临床实践中的性能。结果表明，该方法在处理不完整医学信息时具有较高的准确性、对 BPPV 的良好判别能力和良好的稳健性。通过紧凑的图形表示和推理过程验证了潜在的病理机制和因果语义，增强了诊断结论的可解释性。
