论肿瘤数据智能分析中因果关系的非二进制版本

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS Pub Date : 2024-07-22 DOI:10.3103/S0005105524700146

M. I. Zabezhailo, M. A. Mikheyenkova, Yu. Yu. Trunin

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引用次数: 0

摘要

摘要讨论了在高科技医疗诊断中使用智能数据分析（IDA）的经验和具体情况。当前版本的智能数据分析是通过代数手段对所谓的因果相似性启发式进行数学形式化。针对某些类型的人类脑肿瘤的诊断和治疗任务，展示了所开发方法的主要特点和能力。介绍了一些表征假性进展和肿瘤复发影响因果关系的结果。研究还探讨了所开发的方法和诊断工具在现代循证医学中的潜力和前景。

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On the Nonbinary Version of the Causality Relation in the Intelligent Analysis of Oncological Data

The experience and specifics of the use of intelligent data analysis (IDA) in high-tech medical diagnostics are discussed. The current version of the IDA is a mathematical formalization of the so-called causal similarity heuristic by algebraic means. The main features and abilities of the developed approach are demonstrated in relation to the tasks of the diagnosis and treatment of certain types of human brain tumors. Some results characterizing the causality of the effect of pseudo-progression and tumor recurrence are presented. The potential and prospects of the developed approaches and diagnostic tools in the arsenal of modern evidence-based medicine are considered.

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来源期刊

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS COMPUTER SCIENCE, INFORMATION SYSTEMS-

自引率

40.00%

发文量

期刊介绍： Automatic Documentation and Mathematical Linguistics is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.