Enhancing patient rehabilitation predictions with a hybrid anomaly detection model: Density-based clustering and interquartile range methods

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Murad Ali Khan, Jong-Hyun Jang, Naeem Iqbal, Harun Jamil, Syed Shehryar Ali Naqvi, Salabat Khan, Jae-Chul Kim, Do-Hyeun Kim
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引用次数: 0

Abstract

In recent years, there has been a concerted effort to improve anomaly detection techniques, particularly in the context of high-dimensional, distributed clinical data. Analysing patient data within clinical settings reveals a pronounced focus on refining diagnostic accuracy, personalising treatment plans, and optimising resource allocation to enhance clinical outcomes. Nonetheless, this domain faces unique challenges, such as irregular data collection, inconsistent data quality, and patient-specific structural variations. This paper proposed a novel hybrid approach that integrates heuristic and stochastic methods for anomaly detection in patient clinical data to address these challenges. The strategy combines HPO-based optimal Density-Based Spatial Clustering of Applications with Noise for clustering patient exercise data, facilitating efficient anomaly identification. Subsequently, a stochastic method based on the Interquartile Range filters unreliable data points, ensuring that medical tools and professionals receive only the most pertinent and accurate information. The primary objective of this study is to equip healthcare professionals and researchers with a robust tool for managing extensive, high-dimensional clinical datasets, enabling effective isolation and removal of aberrant data points. Furthermore, a sophisticated regression model has been developed using Automated Machine Learning (AutoML) to assess the impact of the ensemble abnormal pattern detection approach. Various statistical error estimation techniques validate the efficacy of the hybrid approach alongside AutoML. Experimental results show that implementing this innovative hybrid model on patient rehabilitation data leads to a notable enhancement in AutoML performance, with an average improvement of 0.041 in the R 2 ${R}^{2}$ score, surpassing the effectiveness of traditional regression models.

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用混合异常检测模型增强患者康复预测:基于密度的聚类和四分位数范围方法
近年来,人们一直在努力改进异常检测技术,特别是在高维、分布式临床数据的背景下。在临床环境中分析患者数据揭示了对改进诊断准确性、个性化治疗计划和优化资源分配以提高临床结果的显著关注。尽管如此,该领域面临着独特的挑战,例如不规则的数据收集、不一致的数据质量和患者特定的结构变化。本文提出了一种新的混合方法,将启发式和随机方法结合起来,用于患者临床数据的异常检测,以解决这些挑战。该策略将基于hpo的最优基于密度的应用空间聚类与噪声相结合,对患者运动数据进行聚类,从而实现高效的异常识别。随后,基于四分位间距的随机方法过滤不可靠的数据点,确保医疗工具和专业人员只接收到最相关和最准确的信息。本研究的主要目的是为医疗保健专业人员和研究人员提供一个强大的工具,用于管理广泛的高维临床数据集,从而有效地隔离和去除异常数据点。此外,利用自动机器学习(AutoML)开发了一个复杂的回归模型来评估集成异常模式检测方法的影响。各种统计误差估计技术与AutoML一起验证了混合方法的有效性。实验结果表明,将该创新混合模型应用于患者康复数据后,AutoML的性能得到了显著提升,r2 ${R}^{2}$评分平均提升0.041,超过了传统回归模型的有效性。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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