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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
{"title":"Deep Attention Q-Network for Personalized Treatment Recommendation.","authors":"Simin Ma, Junghwan Lee, Nicoleta Serban, Shihao Yang","doi":"10.1109/icdmw60847.2023.00048","DOIUrl":"10.1109/icdmw60847.2023.00048","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":91379,"journal":{"name":"Proceedings ... ICDM workshops. IEEE International Conference on Data Mining","volume":"2023 ","pages":"329-337"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11216720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141478143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Spatio-Temporal Trend Analysis of the Brazilian Elections Based on Twitter Data","authors":"B. Praciano, J. Costa, J. Maranhao, Fábio L. L. Mendonça, Rafael Timóteo de Sousa Júnior, J. Prettz","doi":"10.1109/ICDMW.2018.00192","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00192","url":null,"abstract":"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.","PeriodicalId":91379,"journal":{"name":"Proceedings ... ICDM workshops. IEEE International Conference on Data Mining","volume":"22 1","pages":"1355-1360"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89695799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Process-oriented Iterative Multiple Alignment for Medical Process Mining.","authors":"Shuhong Chen,&nbsp;Sen Yang,&nbsp;Moliang Zhou,&nbsp;Randall S Burd,&nbsp;Ivan Marsic","doi":"10.1109/ICDMW.2017.63","DOIUrl":"10.1109/ICDMW.2017.63","url":null,"abstract":"<p><p>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(N<sup>2</sup>L<sup>2</sup>) 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(NL<sup>2</sup>) 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.</p>","PeriodicalId":91379,"journal":{"name":"Proceedings ... ICDM workshops. IEEE International Conference on Data Mining","volume":"2017 ","pages":"438-445"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICDMW.2017.63","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36664101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Generalized Additive Models from a Neural Network Perspective","authors":"D. D. Waal, J. Toit","doi":"10.1109/ICDMW.2007.127","DOIUrl":"https://doi.org/10.1109/ICDMW.2007.127","url":null,"abstract":"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.","PeriodicalId":91379,"journal":{"name":"Proceedings ... ICDM workshops. IEEE International Conference on Data Mining","volume":"18 1","pages":"265-270"},"PeriodicalIF":0.0,"publicationDate":"2007-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81727585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Data Modeling for Content-Based Support Environment Application on Epilepsy Data Mining","authors":"Mohammad-Reza Siadat, H. Soltanian-Zadeh, F. Fotouhi, Ameen Eetemadi, K. Elisevich","doi":"10.1109/ICDMW.2007.92","DOIUrl":"https://doi.org/10.1109/ICDMW.2007.92","url":null,"abstract":"","PeriodicalId":91379,"journal":{"name":"Proceedings ... ICDM workshops. IEEE International Conference on Data Mining","volume":"115 1","pages":"181-188"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77910518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
{"title":"Reducing the Frequent Pattern Set","authors":"R. Bathoorn, Arne Koopman, A. Siebes","doi":"10.1109/ICDMW.2006.140","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.140","url":null,"abstract":"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.","PeriodicalId":91379,"journal":{"name":"Proceedings ... ICDM workshops. IEEE International Conference on Data Mining","volume":"61 1","pages":"55-59"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80485563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
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