首页|Study Data from SRM Institute of Science and Technology Provide New Insights int o Intelligent Systems (Fuzzy rule based classifier model for evidence based clin ical decision support systems)
Study Data from SRM Institute of Science and Technology Provide New Insights int o Intelligent Systems (Fuzzy rule based classifier model for evidence based clin ical decision support systems)
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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."
SRM Institute of Science and TechnologyTamil NaduIndiaAsiaFuzzy LogicIntelligent SystemsMachine Learning
NETL
NSTL
