摘要
一位新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于智能系统的新报告。根据NewsRx Correspo Ndents来自印度泰米尔纳德邦的消息，研究表明：“临床医生受益于应用于健康记录中的健康数据的人工智能和机器学习技术，这种技术可以识别他们之间的共同点。它使他们能够在推荐未诊断健康记录的共享治疗路径时获得基于证据的支持。”这项研究的财政支持者包括比尔和梅林达·盖茨基金会。我们的新闻记者从SRM科技研究所的研究中得到一句话:“从一系列健康记录中得出的这些模式的集体推论进一步增强了挖掘基本特征的能力,”为支持公共卫生专家对人口健康状况的管理，本文提出了一种新的映射工具模型，旨在分析电子健康记录，为医疗保健提供者提供循证决策支持，重点分析医院数据库中健康记录的分析，包括从常规健康检查中提取的参数，通过对被检查健康记录中的模式进行选择，为医疗保健提供者提供决策支持。该方法的核心是将基于模糊规则的分类器系统集成到系统中,方便了对健康记录的处理,在知识库的支持下提取相关特征以增强决策.该模型结构提供了灵活性和可定制性.使系统易于配置，能够准确地将新的健康记录映射到所检查的数据集。此外，该模型还利用了专门开发的针对所提议的模糊系统的距离测量技术。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on intelligent s ystems. According to news originating from Tamil Nadu, India, by NewsRx correspo ndents, research stated, "Clinicians benefit from the use of artificial intellig ence and machine learning techniques applied to health data within health record s, which identify commonalities between them. It enables them to get evidence-ba sed support in recommending shared treatment paths for undiagnosed health record s." Financial supporters for this research include Bill And Melinda Gates Foundation . Our news reporters obtained a quote from the research from SRM Institute of Scie nce and Technology: "The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential featur es, supporting public health experts in their management of population health co nditions. This paper presents a novel mapping tool model designed to analyze ele ctronic health records and provide healthcare providers with evidence-based deci sion support. The work focuses on the analysis of health records from hospital d atabases, encompassing parameters extracted from routine health checkups. By scr utinizing patterns within examined health records, healthcare providers can seam lessly align with newer health records for diagnosis and treatment recommendatio ns. Core to this approach is the integration of a fuzzy rule-based classifier sy stem within the proposed system. This incorporation facilitates the processing o f health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a spec ially developed distance-measure technique tailored for the proposed fuzzy-based system."
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