一个可解释的基于知识的系统,使用主观偏好和客观数据对决策方案进行排序。

IF 1.8 4区 医学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Methods of Information in Medicine Pub Date : 2022-09-01 Epub Date: 2022-10-11 DOI:10.1055/s-0042-1756650
Kavya Ramisetty, Jabez Christopher, Subhrakanta Panda, Baktha Singh Lazarus, Julie Dayalan
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引用次数: 1

摘要

背景:过敏是过敏原与免疫系统发生反应时发生的超敏反应。在南亚国家,过敏的患病率和严重程度正在上升。过敏通常发生在组合中,这对医生来说很难诊断。目的:本工作旨在建立一个决策模型,以帮助医生诊断过敏合并症。该模型不仅要提供理性的决策,而且要提供关于所有选择的可解释的知识。方法:从实时来源收集的过敏数据包含较少的合并症样本。决策模型采用理想、单一和完整三种抽样策略来平衡数据。利用基于贝叶斯定理的概率方法从平衡数据中提取知识。相对于替代品属性的偏好权重是从隶属于不同过敏测试中心的一组领域专家那里收集的。权重与客观知识相结合,为备选方案分配置信度。该系统提供这些值以及解释,以帮助决策者选择最佳决策。结果:可解释性和用户满意度指标用于评估系统在实时诊断中的有效性。Fleiss的Kappa统计值为0.48,因此专家们的诊断被认为是中等一致的。决策模型提供最多10个合适和相关的证据来解释决策选择。使用CDSS后,临床医生的诊断性能提高了3%(77.93%),所需时间减少了20%。结论:在可解释的决策模型的支持下,经验不足的临床医生的表现得到了改善。包含所有中间结果的框架代码可在https://github.com/kavya6697/Allergy-PT.git上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives.

Background: Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.

Objectives: This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.

Methods: The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.

Results: Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.

Conclusion: The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.

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来源期刊
Methods of Information in Medicine
Methods of Information in Medicine 医学-计算机:信息系统
CiteScore
3.70
自引率
11.80%
发文量
33
审稿时长
6-12 weeks
期刊介绍: Good medicine and good healthcare demand good information. Since the journal''s founding in 1962, Methods of Information in Medicine has stressed the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care. Covering publications in the fields of biomedical and health informatics, medical biometry, and epidemiology, the journal publishes original papers, reviews, reports, opinion papers, editorials, and letters to the editor. From time to time, the journal publishes articles on particular focus themes as part of a journal''s issue.
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