一种用于发现耳科诊断的新型机器学习程序

J. Laurikkala, E. Kentala, M. Juhola, I. Pyykkö
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引用次数: 12

摘要

一种新的机器学习系统,卡拉狄加,已经被开发出来用于从数据库中发现知识。应用该系统从564例前庭神经鞘瘤、阵发性体位性眩晕、梅尼氏病、突发性耳聋、外伤性眩晕和前庭神经炎诊断的患者数据库中发现诊断规则。使用独立测试集对规则进行评估。诊断规则的准确率分别为91%、96%、81%、95%、92%和98%。除了准确之外,这些规则还包含了早期研究中确定的五个最重要的诊断问题。规则所提供的知识易于理解和验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel machine learning program applied to discover otological diagnoses
A novel machine learning system, Galactica, has been developed for knowledge discovery from databases. This system was applied to discover diagnostic rules from a patient database containing 564 cases with vestibular schwannoma, bening paroxysmal positional vertigo, Me´nie`re's disease, sudden deafness, traumatic vertigo and vestibular neuritis diagnoses. The rules were evaluated using an independent testing set. The accuracy of rules for these diagnoses were 91%, 96%, 81%, 95%, 92% and 98%, respectively. Besides being accurate, the rules contained the five most important diagnostic questions identified in the earlier research. The knowledge presented with rules can be easily comprehended and verified.
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