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An application of case-based reasoning with machine learning for forensic autopsy

机译：基于案例的推理与机器学习在法医尸检中的应用

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Case-based reasoning (CBR) is one of the matured paradigms of artificial intelligence for problem solving. CBR has been applied in many areas in the commercial sector to assist daily operations. However, CBR is relatively new in the field of forensic science. Even though forensic personnel have consciously used past experiences in solving new cases, the idea of applying machine intelligence to support decision-making in forensics is still in its infancy and poses a great challenge. This paper highlights the limitation of the methods used in forensics compared with a CBR method in the analysis of forensic evidences. The design and development of an Intelligent Forensic Autopsy Report System (1-AuReSys) basing on a CBR method along with the experimental results are presented. Our system is able to extract features by using an information extraction (IE) technique from the existing autopsy reports; then the system analyzes the case similarities by coupling the CBR technique with a Naive Bayes learner for feature-weights learning; and finally it produces an outcome recommendation. Our experimental results reveal that the CBR method with the implementation of a learner is indeed a viable alternative method to the forensic methods with practical advantages.
机译：基于案例的推理（CBR）是人工智能解决问题的成熟范例之一。社区康复已在商业领域的许多领域得到应用，以协助日常运营。但是，CBR在法医学领域相对较新。尽管法医人员有意识地利用过去的经验来解决新案件，但应用机器智能来支持法医决策的想法仍处于起步阶段，并提出了巨大挑战。本文重点介绍了与法医证据分析中的CBR方法相比，法医使用的方法的局限性。介绍了基于CBR方法的智能法医尸检报告系统（1-AuReSys）的设计与开发以及实验结果。我们的系统能够通过使用信息提取（IE）技术从现有的尸检报告中提取特征；然后系统通过将CBR技术与Naive Bayes学习器结合进行特征权重学习来分析案例相似性；最后产生结果建议。我们的实验结果表明，结合学习者的CBR方法确实是具有实际优势的法医方法的可行替代方法。

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