School of ITEE, The University of Queensland, St. Lucia, Queensland, Australia.
PLoS One. 2012;7(11):e50614. doi: 10.1371/journal.pone.0050614. Epub 2012 Nov 30.
A lack of mature domain knowledge and well established guidelines makes the medical diagnosis of skeletal dysplasias (a group of rare genetic disorders) a very complex process. Machine learning techniques can facilitate objective interpretation of medical observations for the purposes of decision support. However, building decision support models using such techniques is highly problematic in the context of rare genetic disorders, because it depends on access to mature domain knowledge. This paper describes an approach for developing a decision support model in medical domains that are underpinned by relatively sparse knowledge bases. We propose a solution that combines association rule mining with the Dempster-Shafer theory (DST) to compute probabilistic associations between sets of clinical features and disorders, which can then serve as support for medical decision making (e.g., diagnosis). We show, via experimental results, that our approach is able to provide meaningful outcomes even on small datasets with sparse distributions, in addition to outperforming other Machine Learning techniques and behaving slightly better than an initial diagnosis by a clinician.
缺乏成熟的领域知识和完善的指导方针使得骨骼发育不良（一组罕见的遗传疾病）的医学诊断成为一个非常复杂的过程。机器学习技术可以促进对医疗观察结果进行客观解释，以便提供决策支持。然而，在罕见遗传疾病的背景下，使用此类技术构建决策支持模型存在很大问题，因为这取决于是否能够获得成熟的领域知识。本文描述了一种在相对稀疏的知识库支持下开发医学领域决策支持模型的方法。我们提出了一种解决方案，将关联规则挖掘与 Dempster-Shafer 理论（DST）相结合，以计算临床特征和疾病之间的概率关联，然后这些关联可以作为医疗决策的支持（例如诊断）。通过实验结果，我们表明，即使在数据集较小且分布稀疏的情况下，我们的方法也能够提供有意义的结果，此外，它的性能优于其他机器学习技术，并且比临床医生的初步诊断稍好一些。
