2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第8页

Reduction of Hospital Readmissions through Clustering Based Actionable Knowledge Mining 基于聚类的可操作知识挖掘减少医院再入院

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0071

M. Al-Mardini, Ayman Hajja, L. Clover, D. Olaleye, Youngjin Park, Jay Paulson, Yang Xiao

{"title":"Reduction of Hospital Readmissions through Clustering Based Actionable Knowledge Mining","authors":"M. Al-Mardini, Ayman Hajja, L. Clover, D. Olaleye, Youngjin Park, Jay Paulson, Yang Xiao","doi":"10.1109/WI.2016.0071","DOIUrl":"https://doi.org/10.1109/WI.2016.0071","url":null,"abstract":"Healthcare spending has been increasing in the last few decades. One of the main reasons for this increase is hospital readmissions, which is defined as a re-hospitalization of a patient after being discharged from a hospital within a short period of time. The excessive amount of money spent every year on hospital readmissions and the urge to enhance healthcare quality make reducing hospital readmissions a necessity. In this paper, we extract knowledge from a medical dataset and apply the concept of mining actionable rules to guide the health domain experts in their decision-making process. We present novel algorithms to increase the predictability of the patients' paths (the sequence of procedures that patients undertakes to reach a desired treatment) by clustering the patients according to their set of diagnoses. Moreover, we present a scoring metric to evaluate procedures in procedure graphs (the tree of all possible procedure paths) and a scoring metric to evaluate clusters of diagnoses which would allow us to anticipate the number of following readmissions for a new patient. Finally, we present an algorithm to evaluate the score (average number of following readmissions) for new patients prior to applying the action rules and after. The results presented in this paper show that our algorithms are able to reduce the average number of readmissions to a high degree.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"26 1","pages":"444-448"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90707020","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: 12

A Basic Study on Spoiler Detection from Review Comments Using Story Documents 基于故事文档的评论剧透检测的基础研究

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0098

Kyosuke Maeda, Y. Hijikata, Satoshi Nakamura

引用次数: 6

"Best Dinner Ever!!!": Automatic Generation of Restaurant Reviews with LSTM-RNN “史上最棒的晚餐!!”:基于LSTM-RNN的餐厅评论自动生成

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0130

Alberto Bartoli, A. D. Lorenzo, Eric Medvet, D. Morello, F. Tarlao

引用次数: 10

Arabic Ontology Learning from Un-structured Text 从非结构化文本学习阿拉伯语本体

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0082

Saeed Al-Bukhitan, T. Helmy

引用次数: 6

On Learning and Co-learning Effective Strategies in Iterated Travelers' Dilemma 迭代旅行者困境中的学习与共同学习有效策略研究

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0120

Predrag T. Tosic

{"title":"On Learning and Co-learning Effective Strategies in Iterated Travelers' Dilemma","authors":"Predrag T. Tosic","doi":"10.1109/WI.2016.0120","DOIUrl":"https://doi.org/10.1109/WI.2016.0120","url":null,"abstract":"In this short paper, we summarize our previous results, and share some new insights, ideas and challenges about what types of adaptable strategies generally tend to get rewarded in Iterated Travelers' Dilemma (ITD). Our primary motivation for studying ITD is that this strategic 2-person game provides implicit incentives for cooperation – but only if both players cooperate. ITD is a non-zero-sum two-player game that generalizes the better known Iterated Prisoner's Dilemma (IPD). Both IPD and ITD can be viewed as repeated exchanges of proposals or bids, where the payoff to each agent at the end of a round is based on i) how close were the two agents' bids to each other and ii) who bid lower in that round. Our broader goal is to understand how a resource-bounded rational agent can learn about the behavior of other self-interested agents, in order to adjust his or her own bidding strategy in a manner that is most likely to be rewarding in the long run. In addition to exploring traditional reinforcement learning mechanisms in this setting, we also initiate studying the potential promise of co-learning between pairs of adaptive, self-interested but non-malicious agents with bounded computational resources.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"51 1","pages":"674-677"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83297280","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

An Heuristics-Based, Weakly-Supervised Approach for Classification of Stance in Tweets 基于启发式、弱监督的推文立场分类方法

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0021

Marcelo Dias, Karin Becker

引用次数: 2

Leveraging Item Connections to Improve Social Recommendations with Ratings and Reviews 利用项目连接提高社会推荐与评级和评论

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0035

Jiajin Huang, N. Zhong

引用次数: 3

Practical Web Data Extraction: Are We There Yet? - A Short Survey 实用的网络数据提取:我们到了吗?-简短调查

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0096

Andreas Schulz, Jörg Lässig, M. Gaedke

引用次数: 13

A Social Curiosity Inspired Recommendation Model to Improve Precision, Coverage and Diversity 基于社交好奇心的推荐模型提高准确性、覆盖面和多样性

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0042

Qiong Wu, Siyuan Liu, C. Miao, Y. Liu, Cyril Leung

引用次数: 16

Statistical-Based Image Tagging 基于统计的图像标注

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0106

M. Masoud, Sanghoon Lee, S. Belkasim

引用次数: 2