一种用于改进患者分诊的新型网络科学算法
A novel Network Science Algorithm for Improving Triage of Patients
AI总结：
本文提出一种基于患者数据分析的新型网络科学分诊算法，通过预处理和特征工程实现高准确率分类，优于传统方法，有望提升医疗分诊效率与资源分配。
摘要：
患者分诊在医疗保健中起着至关重要的作用，它根据患者病情的紧急程度确保及时和适当的护理。传统的分诊方法严重依赖人工判断，这可能具有主观性且容易出错。近来，人们越来越关注利用人工智能（AI）开发用于患者分诊的算法。本文介绍了一种用于患者分诊的新型算法的开发。该算法基于对患者数据的分析来产生关于其优先级的决策。该算法在一个包含相关患者信息（如生命体征、症状和病史）的综合数据集上进行了训练。通过严格的预处理和特征工程，该算法被设计为能够准确地将患者分类到分诊类别中。实验结果表明，我们的算法实现了高准确率和性能，优于传统的分诊方法。通过将计算机科学纳入分诊过程，医疗保健专业人员可以受益于效率、准确性和一致性的提高，从而有效地确定患者优先级并优化资源分配。尽管还需要进一步研究来解决训练数据中的偏差和模型可解释性等挑战，但基于AI的患者分诊算法的开发在增强医疗保健服务和改善患者预后方面显示出巨大潜力。
英文摘要：
Patient triage plays a crucial role in healthcare, ensuring timely and appropriate care based on the urgency of patient conditions. Traditional triage methods heavily rely on human judgment, which can be subjective and prone to errors. Recently, a growing interest has been in leveraging artificial intelligence (AI) to develop algorithms for triaging patients. This paper presents the development of a novel algorithm for triaging patients. It is based on the analysis of patient data to produce decisions regarding their prioritization. The algorithm was trained on a comprehensive data set containing relevant patient information, such as vital signs, symptoms, and medical history. The algorithm was designed to accurately classify patients into triage categories through rigorous preprocessing and feature engineering. Experimental results demonstrate that our algorithm achieved high accuracy and performance, outperforming traditional triage methods. By incorporating computer science into the triage process, healthcare professionals can benefit from improved efficiency, accuracy, and consistency, prioritizing patients effectively and optimizing resource allocation. Although further research is needed to address challenges such as biases in training data and model interpretability, the development of AI-based algorithms for triaging patients shows great promise in enhancing healthcare delivery and patient outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
