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Towards Using Rule-Based Multi-Agent System for the Early Detection of Adverse Drug Reactions

机译:朝向利用基于规则的多助剂系统进行不良药物反应的早期检测

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Adverse Drug Reactions (ADRs) represent troublesome and potentially fatal side effects of medication treatment. To address the burden induced by ADRs, a preventive approach is necessary whereby clinicians are provided with new data-driven decision-support systems to foresee the factors leading to ADRs and plan precautionary activities effectively. We develop a multi-agent system which monitors the factors leading to the onset of ADRs using information found in the patient records in a hospital setting. The system uses a fuzzy rule-based reasoning engine utilising decision rules developed by clinicians. We evaluate the ability of the framework to identify the cause of ADRs from patient records in a case study involving records of metal health patients. Our work is the first preventive agent-based aid tool.
机译:不良药物反应(ADRS)代表药物治疗的麻烦和潜在的致命副作用。 为了解决ADRS引起的负担,需要一种预防方法,即临床医生提供新的数据驱动决策支持系统,以预见到有效的导致ADRS和计划预防活动的因素。 我们开发了一个多代理系统,可以使用医院环境中的患者记录中发现的信息监控导致ADR的发出的因素。 该系统采用了利用临床医生开发的决策规则的模糊规则的推理引擎。 在涉及金属健康患者记录的案例研究中,我们评估框架从患者记录中识别ADRS原因的能力。 我们的作品是第一个预防性代理的辅助工具。

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