Schedule management of hospitalization is important to maintain or improve the quality of medical care. Application of a clinical pathway has been proposed as one of the important solutions for the management. This research proposed an data-oriented maintenance and construction of clinical pathways by using data on histories of nursing orders stored in hospital information system.. The method was evaluated on data extracted from a hospital information system. The results show that the reuse of stored data will give a powerful tool for management of nursing schedule and lead to improvement of hospital services.
{"title":"Mining nursing care plan from data extracted from hospital information system","authors":"S. Tsumoto, S. Hirano, H. Iwata","doi":"10.1145/2492517.2500332","DOIUrl":"https://doi.org/10.1145/2492517.2500332","url":null,"abstract":"Schedule management of hospitalization is important to maintain or improve the quality of medical care. Application of a clinical pathway has been proposed as one of the important solutions for the management. This research proposed an data-oriented maintenance and construction of clinical pathways by using data on histories of nursing orders stored in hospital information system.. The method was evaluated on data extracted from a hospital information system. The results show that the reuse of stored data will give a powerful tool for management of nursing schedule and lead to improvement of hospital services.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117005417","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}
Discovering similar objects in a social network has many interesting issues. Here, we present ASCOS, an Asymmetric Structure COntext Similarity measure that captures the similarity scores among any pairs of nodes in a network. The definition of ASCOS is similar to that of the well-known SimRank since both define score values recursively. However, we show that ASCOS outputs a more complete similarity score than SimRank because SimRank (and several of its variations, such as P-Rank and SimFusion) on average ignores half paths between nodes during calculation. To make ASCOS tractable in both computation time and memory usage, we propose two variations of ASCOS: a low rank approximation based approach and an iterative solver Gauss-Seidel for linear equations.When the target network is sparse, the run time and the required computing space of these variations are smaller than computing SimRank and ASCOS directly. In addition, the iterative solver divides the original network into several independent sub-systems so that a multi-core server or a distributed computing environment, such as MapReduce, can efficiently solve the problem. We compare the performance of ASCOS with other global structure based similarity measures, including SimRank, Katz, and LHN. The experimental results based on user evaluation suggest that ASCOS gives better results than other measures. In addition, the asymmetric property has the potential to identify the hierarchical structure of a network. Finally, variations of ASCOS (including one distributed variation) can also reduce computation both in space and time.
{"title":"ASCOS: An Asymmetric network Structure COntext Similarity measure","authors":"Hung-Hsuan Chen, C. Lee Giles","doi":"10.1145/2492517.2492539","DOIUrl":"https://doi.org/10.1145/2492517.2492539","url":null,"abstract":"Discovering similar objects in a social network has many interesting issues. Here, we present ASCOS, an Asymmetric Structure COntext Similarity measure that captures the similarity scores among any pairs of nodes in a network. The definition of ASCOS is similar to that of the well-known SimRank since both define score values recursively. However, we show that ASCOS outputs a more complete similarity score than SimRank because SimRank (and several of its variations, such as P-Rank and SimFusion) on average ignores half paths between nodes during calculation. To make ASCOS tractable in both computation time and memory usage, we propose two variations of ASCOS: a low rank approximation based approach and an iterative solver Gauss-Seidel for linear equations.When the target network is sparse, the run time and the required computing space of these variations are smaller than computing SimRank and ASCOS directly. In addition, the iterative solver divides the original network into several independent sub-systems so that a multi-core server or a distributed computing environment, such as MapReduce, can efficiently solve the problem. We compare the performance of ASCOS with other global structure based similarity measures, including SimRank, Katz, and LHN. The experimental results based on user evaluation suggest that ASCOS gives better results than other measures. In addition, the asymmetric property has the potential to identify the hierarchical structure of a network. Finally, variations of ASCOS (including one distributed variation) can also reduce computation both in space and time.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117231468","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}
The problem of Twitter user classification using the contents of tweets is studied. We generate time series from tweets by exploiting the latent temporal information and solve the classification problem in time series domain. Our approach is inspired by the fact that Twitter users sometimes exhibit the periodicity pattern when they share their activities or express their opinions. We apply our proposed methods to both binary and multi-class classification of sports and political interests of Twitter users and compare the performance against eight conventional classification methods using textual features. Experimental results using 2.56 million tweets show that our best binary and multi-class approaches improve the classification accuracy over the best baseline binary and multi-class approaches by 15% and 142%, respectively.
{"title":"Steeler nation, 12th man, and boo birds: Classifying Twitter user interests using time series","authors":"Tao Yang, Dongwon Lee, Su Yan","doi":"10.1145/2492517.2492551","DOIUrl":"https://doi.org/10.1145/2492517.2492551","url":null,"abstract":"The problem of Twitter user classification using the contents of tweets is studied. We generate time series from tweets by exploiting the latent temporal information and solve the classification problem in time series domain. Our approach is inspired by the fact that Twitter users sometimes exhibit the periodicity pattern when they share their activities or express their opinions. We apply our proposed methods to both binary and multi-class classification of sports and political interests of Twitter users and compare the performance against eight conventional classification methods using textual features. Experimental results using 2.56 million tweets show that our best binary and multi-class approaches improve the classification accuracy over the best baseline binary and multi-class approaches by 15% and 142%, respectively.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"521 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116198583","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}
User attribute is an essential factor for personalized recommendation and targeted advertising. Therefore, there have been a number of studies to identify user attributes automatically from SNS postings, since the postings reveal various attributes of writers. Many kinds of machine learning methods have been applied to automatic identification of user attributes as a candidate solution, but they suffer from two major problems. First, there are many postings in SNS that do not deliver any information about writers. Then, learning from SNS postings results in a biased model by these irrelevant postings. Second, the postings of a SNS user are somewhat related one another. However, most machine learning methods ignore this information, since they assume that data are independently and identically distributed. In order to solve these problems in user attribute identification, this paper proposes a novel method based on non-i.i.d. multi-instance learning. Since multi-instance learning treats all postings by a user as a bag and learns user attribute identification with such bags, not with postings, the first problem is solved. In addition, the proposed method assumes that the postings by a single user have a structure. By incorporating this assumption into the multi-instance learning, the second problem is solved. Our experimental results show that consideration of these two problems in automatic user attribute identification results in performance improvement.
{"title":"Identifying user attributes through non-i.i.d. multi-instance learning","authors":"Hyun-Je Song, J. Son, Seong-Bae Park","doi":"10.1145/2492517.2492597","DOIUrl":"https://doi.org/10.1145/2492517.2492597","url":null,"abstract":"User attribute is an essential factor for personalized recommendation and targeted advertising. Therefore, there have been a number of studies to identify user attributes automatically from SNS postings, since the postings reveal various attributes of writers. Many kinds of machine learning methods have been applied to automatic identification of user attributes as a candidate solution, but they suffer from two major problems. First, there are many postings in SNS that do not deliver any information about writers. Then, learning from SNS postings results in a biased model by these irrelevant postings. Second, the postings of a SNS user are somewhat related one another. However, most machine learning methods ignore this information, since they assume that data are independently and identically distributed. In order to solve these problems in user attribute identification, this paper proposes a novel method based on non-i.i.d. multi-instance learning. Since multi-instance learning treats all postings by a user as a bag and learns user attribute identification with such bags, not with postings, the first problem is solved. In addition, the proposed method assumes that the postings by a single user have a structure. By incorporating this assumption into the multi-instance learning, the second problem is solved. Our experimental results show that consideration of these two problems in automatic user attribute identification results in performance improvement.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123370385","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}
Optometry is an essential health care profession that has existed for many centuries and is still evolving. However, the training approaches for optometrists are not yet on par with the latest technological evolution. The traditional supervisor-student training mode could not provide good immersion and repeatability, while most existing vision-based computer-assisted simulations provide even worse immersion on screens. In this paper, we propose an effective system for optometry training simulation with two major components: augmented reality and haptics. These components are integrated with the actual slit lamp and are able to greatly enhance the immersion for typical optometry training tasks such as foreign body removal. Medical doctors are also involved in suggesting configurations and validating visual and haptic rendering results. Preliminary user studies show very positive feedbacks from optometry students.
{"title":"Optometry training simulation with augmented reality and haptics","authors":"Lei Wei, S. Nahavandi, H. Weisinger","doi":"10.1145/2492517.2500326","DOIUrl":"https://doi.org/10.1145/2492517.2500326","url":null,"abstract":"Optometry is an essential health care profession that has existed for many centuries and is still evolving. However, the training approaches for optometrists are not yet on par with the latest technological evolution. The traditional supervisor-student training mode could not provide good immersion and repeatability, while most existing vision-based computer-assisted simulations provide even worse immersion on screens. In this paper, we propose an effective system for optometry training simulation with two major components: augmented reality and haptics. These components are integrated with the actual slit lamp and are able to greatly enhance the immersion for typical optometry training tasks such as foreign body removal. Medical doctors are also involved in suggesting configurations and validating visual and haptic rendering results. Preliminary user studies show very positive feedbacks from optometry students.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128137359","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}
The existence of social media has made computer-mediated communication more widespread among users around the world. This paper describes the development of an emoticon recommendation system that allows users to express their feelings with their input. In order to develop this system, an innovative emoticon database consisting of a table of emoticons with points expressed from each of 10 distinctive emotions was constructed. An evaluation experiment showed that 71.3% of user-selected emoticons were among the top 10 emoticons recommended by the proposed system.
{"title":"Emoticon recommendation system for effective communication","authors":"Yuki Urabe, Rafal Rzepka, K. Araki","doi":"10.1145/2492517.2492594","DOIUrl":"https://doi.org/10.1145/2492517.2492594","url":null,"abstract":"The existence of social media has made computer-mediated communication more widespread among users around the world. This paper describes the development of an emoticon recommendation system that allows users to express their feelings with their input. In order to develop this system, an innovative emoticon database consisting of a table of emoticons with points expressed from each of 10 distinctive emotions was constructed. An evaluation experiment showed that 71.3% of user-selected emoticons were among the top 10 emoticons recommended by the proposed system.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126041086","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}
Jordi Casas-Roma, J. Herrera-Joancomartí, V. Torra
In this paper, we consider the problem of anonymization on large networks. There are some anonymization methods for networks, but most of them can not be applied on large networks because of their complexity. We present an algorithm for k-degree anonymity on large networks. Given a network G, we construct a k-degree anonymous network, G̃, by the minimum number of edge modifications. We devise a simple and efficient algorithm for solving this problem on large networks. Our algorithm uses univariate micro-aggregation to anonymize the degree sequence, and then it modifies the graph structure to meet the k-degree anonymous sequence. We apply our algorithm to a different large real datasets and demonstrate their efficiency and practical utility.
{"title":"An algorithm for k-degree anonymity on large networks","authors":"Jordi Casas-Roma, J. Herrera-Joancomartí, V. Torra","doi":"10.1145/2492517.2492643","DOIUrl":"https://doi.org/10.1145/2492517.2492643","url":null,"abstract":"In this paper, we consider the problem of anonymization on large networks. There are some anonymization methods for networks, but most of them can not be applied on large networks because of their complexity. We present an algorithm for k-degree anonymity on large networks. Given a network G, we construct a k-degree anonymous network, G̃, by the minimum number of edge modifications. We devise a simple and efficient algorithm for solving this problem on large networks. Our algorithm uses univariate micro-aggregation to anonymize the degree sequence, and then it modifies the graph structure to meet the k-degree anonymous sequence. We apply our algorithm to a different large real datasets and demonstrate their efficiency and practical utility.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122480534","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}
In this paper, we predict hashtag trend in Twitter network with two basic issues under investigation, i.e. trend factors and prediction models. To address the first issue, we consider different content and context factors by designing features from tweet messages, network topology, user behavior, etc. To address the second issue, we adopt prediction models that have different combinations of the two basic model properties, i.e. linearity and state-space. Experiments on large Twitter dataset show that both content and context factors can help trend prediction. However, the most relevant factors are derived from user behaviors on the specific trend. Non-linear models are significantly better than their linear counterparts, which can be further slightly improved by the adoption of state-space models.
{"title":"On predicting Twitter trend: Factors and models","authors":"Peng Zhang, Xufei Wang, Baoxin Li","doi":"10.1145/2492517.2492576","DOIUrl":"https://doi.org/10.1145/2492517.2492576","url":null,"abstract":"In this paper, we predict hashtag trend in Twitter network with two basic issues under investigation, i.e. trend factors and prediction models. To address the first issue, we consider different content and context factors by designing features from tweet messages, network topology, user behavior, etc. To address the second issue, we adopt prediction models that have different combinations of the two basic model properties, i.e. linearity and state-space. Experiments on large Twitter dataset show that both content and context factors can help trend prediction. However, the most relevant factors are derived from user behaviors on the specific trend. Non-linear models are significantly better than their linear counterparts, which can be further slightly improved by the adoption of state-space models.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125707942","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}
Microblogging services like Twitter are used for a wide variety of purposes and in different modes. Here, we focus on the usage of Twitter for “chatter” i.e., the production and consumption of tweets that are typically non-topical and contain personal status updates or conversational messages which are usually intended and are useful only to the immediate network of the producers of the tweets. The automatic identification of chatter tweets is critical for tasks such as ranking tweets by relevance, matching tweets to advertisements, creation of topical digests of tweets, etc. and generally improves the utility of tweets to people outside the producers' immediate network by enabling the filtering out of tweets that are not of wider interest. We study the prevalence of chatter tweets in Twitter and present techniques to detect them using machine learning techniques that require minimal supervision.
{"title":"“w00t! feeling great today!” chatter in Twitter: identification and prevalence","authors":"Ramnath Balasubramanyan, A. Kolcz","doi":"10.1145/2492517.2492611","DOIUrl":"https://doi.org/10.1145/2492517.2492611","url":null,"abstract":"Microblogging services like Twitter are used for a wide variety of purposes and in different modes. Here, we focus on the usage of Twitter for “chatter” i.e., the production and consumption of tweets that are typically non-topical and contain personal status updates or conversational messages which are usually intended and are useful only to the immediate network of the producers of the tweets. The automatic identification of chatter tweets is critical for tasks such as ranking tweets by relevance, matching tweets to advertisements, creation of topical digests of tweets, etc. and generally improves the utility of tweets to people outside the producers' immediate network by enabling the filtering out of tweets that are not of wider interest. We study the prevalence of chatter tweets in Twitter and present techniques to detect them using machine learning techniques that require minimal supervision.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688425","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}
This article describes an automated technique that allows to differentiate texts expressing a positive or a negative opinion. The basic principle is based on the observation that positive texts are statistically shorter than negative ones. From this observation of the psycholinguistic human behavior, we derive a heuristic that is employed to generate connoted lexicons with a low level of prior knowledge. The lexicon is then used to compute the level of opinion of an unknown text. Our primary goal is to reduce the need of the human implication (domain and language) in the generation of the lexicon in order to have a process with the highest possible autonomy. The resulting adaptability would represent an advantage with free or approximate expression commonly found in social networks environment.
{"title":"Using the length of the speech to measure the opinion","authors":"L. Lancieri, E. Leprêtre","doi":"10.1145/2492517.2492647","DOIUrl":"https://doi.org/10.1145/2492517.2492647","url":null,"abstract":"This article describes an automated technique that allows to differentiate texts expressing a positive or a negative opinion. The basic principle is based on the observation that positive texts are statistically shorter than negative ones. From this observation of the psycholinguistic human behavior, we derive a heuristic that is employed to generate connoted lexicons with a low level of prior knowledge. The lexicon is then used to compute the level of opinion of an unknown text. Our primary goal is to reduce the need of the human implication (domain and language) in the generation of the lexicon in order to have a process with the highest possible autonomy. The resulting adaptability would represent an advantage with free or approximate expression commonly found in social networks environment.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131001317","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}
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