Proceedings ... ICDM workshops. IEEE International Conference on Data Mining最新文献

英文中文

Deep Attention Q-Network for Personalized Treatment Recommendation. 用于个性化治疗推荐的深度注意力 Q 网络。

Proceedings ... ICDM workshops. IEEE International Conference on Data Mining

Pub Date : 2023-12-01 Epub Date: 2024-02-06 DOI: 10.1109/icdmw60847.2023.00048

Simin Ma, Junghwan Lee, Nicoleta Serban, Shihao Yang

Tailoring treatment for severely ill patients is crucial yet challenging to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations. However, they often rely solely on a patient's current physiological state, which may not accurately represent the true health status of the patient. This limitation hampers policy learning and evaluation, undermining the effectiveness of the treatment. In this study, we propose Deep Attention Q-Network for personalized treatment recommendation, leveraging the Transformer architecture within a deep reinforcement learning framework to efficiently integrate historical observations of patients. We evaluated our proposed method on two real-world datasets: sepsis and acute hypotension patients, demonstrating its superiority over state-of-the-art methods. The source code for our model is available at https://github.com/stevenmsm/RL-ICU-DAQN.

为重症患者量身定制治疗方案对于实现最佳医疗效果至关重要，但也极具挑战性。强化学习的最新进展为个性化治疗建议提供了良好的前景。然而，它们通常仅依赖于患者当前的生理状态，而这可能无法准确代表患者的真实健康状况。这一局限性妨碍了政策学习和评估，从而削弱了治疗的有效性。在本研究中，我们提出了用于个性化治疗推荐的深度注意力 Q 网络，利用深度强化学习框架中的 Transformer 架构来有效整合对患者的历史观察。我们在脓毒症和急性低血压患者这两个真实世界数据集上评估了我们提出的方法，证明它优于最先进的方法。我们模型的源代码可在 https://github.com/stevenmsm/RL-ICU-DAQN 上获取。

引用次数: 0

Spatio-Temporal Trend Analysis of the Brazilian Elections Based on Twitter Data 基于Twitter数据的巴西大选时空趋势分析

Proceedings ... ICDM workshops. IEEE International Conference on Data Mining

Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00192

B. Praciano, J. Costa, J. Maranhao, Fábio L. L. Mendonça, Rafael Timóteo de Sousa Júnior, J. Prettz

Text classification techniques and sentiment analysis can be applied to understand and predict the behavior of users by exploiting the massive amount of data available on social networks. In this context, trend analysis tools based on supervised machine learning are crucial. In this work, a framework for spatio-temporal trend analysis of Brazilian presidential election trends based on Twitter data is proposed. Experimental results show that the proposed framework presents good effectiveness in predicting election results as well as providing tweet author's geolocation and tweet timestamp. According to our results the spatio trend analysis applying our framework via SVM on the Twitter data returns an accuracy close to 90% when the Support Vector Machine (SVM) algortihm is applied for sentiment classification.

文本分类技术和情感分析可以通过利用社交网络上的大量可用数据来理解和预测用户的行为。在这种情况下，基于监督机器学习的趋势分析工具至关重要。在这项工作中，提出了一个基于Twitter数据的巴西总统选举趋势时空趋势分析框架。实验结果表明，该框架在预测选举结果以及提供推文作者地理位置和推文时间戳方面具有良好的有效性。根据我们的结果，当使用支持向量机(SVM)算法进行情感分类时，将我们的框架通过支持向量机(SVM)应用于Twitter数据的空间趋势分析返回的准确率接近90%。

引用次数: 11

Process-oriented Iterative Multiple Alignment for Medical Process Mining. 用于医疗过程挖掘的面向过程的迭代多重对齐。

Proceedings ... ICDM workshops. IEEE International Conference on Data Mining

Pub Date : 2017-11-01 Epub Date: 2017-12-18 DOI: 10.1109/ICDMW.2017.63

Shuhong Chen, Sen Yang, Moliang Zhou, Randall S Burd, Ivan Marsic

Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N²L²) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL²) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.

轨迹比对是一种过程挖掘技术，适用于生物序列比对，用于可视化和分析工作流数据。但是，使用此方法进行的任何分析都会受到路线质量的影响。现有的最佳轨迹对准技术使用渐进引导树来启发式地近似O（N2L2）时间内的最佳对准。这些算法在很大程度上依赖于所选的引导树度量，通常返回成对和分数，从而减少干扰解释的错误，并且对于大型数据集来说计算密集。为了缓解这些问题，我们提出了面向过程的迭代多重对齐（PIMA），它包含专门的优化，以更好地处理工作流数据。我们证明了PIMA是一个灵活的框架，能够在仅O（NL2）时间内实现比现有轨迹对齐算法更好的对和分数。我们将PIMA应用于分析医疗工作流程数据，展示了迭代对齐如何更好地表示数据，并促进从数据可视化中提取见解。

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引用次数: 11

Generalized Additive Models from a Neural Network Perspective 神经网络视角下的广义加性模型

Proceedings ... ICDM workshops. IEEE International Conference on Data Mining

Pub Date : 2007-10-28 DOI: 10.1109/ICDMW.2007.127

D. D. Waal, J. Toit

Recently, an interactive algorithm was proposed for the construction of generalized additive neural networks. Although the proposed method is sound, it has two drawbacks. It is subjective as it relies on the modeler to identify complex trends in partial residual plots and it can be very time consuming as multiple iterations of pruning and adding neurons to hidden layers of the neural network have to be done. In this article, an automatic algorithm is proposed that alleviates both drawbacks. Given a predictive modeling problem, the proposed strategy uses heuristic methods to identify optimal or near optimal generalized additive neural network topologies that are trained to compute the generalized additive model. The neural network approach is conceptually much simpler than many of the other approaches. It is also more accurate as heuristic methods are only used in identifying the appropriate neural network topologies and not in computing the generalized additive models.

最近，提出了一种用于构造广义加性神经网络的交互算法。虽然提出的方法是合理的，但它有两个缺点。它是主观的，因为它依赖于建模器来识别部分残差图中的复杂趋势，并且它可能非常耗时，因为必须完成多次修剪和向神经网络的隐藏层添加神经元的迭代。在本文中，提出了一种自动算法来缓解这两个缺点。给定一个预测建模问题，提出的策略使用启发式方法来识别最优或接近最优的广义加性神经网络拓扑，这些拓扑被训练来计算广义加性模型。神经网络方法在概念上比许多其他方法简单得多。由于启发式方法仅用于识别适当的神经网络拓扑，而不用于计算广义加性模型，因此它也更准确。

引用次数: 19

Data Modeling for Content-Based Support Environment Application on Epilepsy Data Mining 基于内容的支持环境在癫痫数据挖掘中的应用

Proceedings ... ICDM workshops. IEEE International Conference on Data Mining

Pub Date : 2007-01-01 DOI: 10.1109/ICDMW.2007.92

Mohammad-Reza Siadat, H. Soltanian-Zadeh, F. Fotouhi, Ameen Eetemadi, K. Elisevich

引用次数: 0

Reducing the Frequent Pattern Set 减少频繁模式集

Proceedings ... ICDM workshops. IEEE International Conference on Data Mining

Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.140

R. Bathoorn, Arne Koopman, A. Siebes

One of the major problems in frequent pattern mining is the explosion of the number of results, making it difficult to identify the interesting frequent patterns. In a recent paper [7] we have shown that an MDL-based approach gives a dramatic reduction of the number of frequent item sets to consider. Here we show that MDL gives similarly good reductions for frequent patterns on other types of data, viz., on sequences and trees. Reductions of two to three orders of magnitude are easily attained on data sets from the web-mining field.

频繁模式挖掘的主要问题之一是结果数量的爆炸式增长，这使得识别有趣的频繁模式变得困难。在最近的一篇论文b[7]中，我们已经表明，基于mdl的方法可以显著减少需要考虑的频繁项集的数量。在这里，我们展示了MDL对其他类型的数据(即序列和树)上的频繁模式给出了类似的良好约简。在网络挖掘领域的数据集上，很容易实现两到三个数量级的缩减。

引用次数: 20

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