In this paper we discover temporal relations in patient discharge summaries, when the relevant medical events and temporal expressions were provided in the training data.
本文研究了在训练数据中提供相关医疗事件和时间表达式时，患者出院摘要中的时间关系。
{"title":"Temporal Relation Extraction from Medical Discharge Summaries","authors":"E. Silgard, Melissa Tharp, Rutu Mulkar-Mehta","doi":"10.1109/HISB.2012.57","DOIUrl":"https://doi.org/10.1109/HISB.2012.57","url":null,"abstract":"In this paper we discover temporal relations in patient discharge summaries, when the relevant medical events and temporal expressions were provided in the training data.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115575182","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 paper proposes the method called Feature Selection based on the Compactness Measure from Scatterplots (FSCoMS) to select the best features extracted from medical images aiming at improving the effectiveness of Content-Based Image Retrieval. This feature selection algorithm consists in a compactness analysis of scatterplots to find the most relevant features providing high separability abilities. A high relevance value of a scatterplot means better predictability among of classes based on two features. We take advantage of this information to generate a ranking for features usefulness. We compared our method to two well-known feature selection methods using three real medical datasets. All of them were compared regarding the dimensionality of the final feature vector and the retrieval effectiveness measured by the precision and recall graphs. The performed experiments show that our method not only obtained the highest retrieval performance but also achieved the smallest number of demanded features (dimensionality) than the other methods analyzed.
{"title":"FSCoMS: Feature Selection of Medical Images Based on Compactness Measure from Scatterplots","authors":"Gabriel Humpire-Mamani, A. Traina, C. Traina","doi":"10.1109/HISB.2012.29","DOIUrl":"https://doi.org/10.1109/HISB.2012.29","url":null,"abstract":"This paper proposes the method called Feature Selection based on the Compactness Measure from Scatterplots (FSCoMS) to select the best features extracted from medical images aiming at improving the effectiveness of Content-Based Image Retrieval. This feature selection algorithm consists in a compactness analysis of scatterplots to find the most relevant features providing high separability abilities. A high relevance value of a scatterplot means better predictability among of classes based on two features. We take advantage of this information to generate a ranking for features usefulness. We compared our method to two well-known feature selection methods using three real medical datasets. All of them were compared regarding the dimensionality of the final feature vector and the retrieval effectiveness measured by the precision and recall graphs. The performed experiments show that our method not only obtained the highest retrieval performance but also achieved the smallest number of demanded features (dimensionality) than the other methods analyzed.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127067073","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}
Systems that exploit publicly available user generated content such as Twitter messages have been successful in tracking seasonal influenza. We developed a novel filtering method for Influenza-Like-Ilnesses (ILI)-related messages using 587 million messages from Twitter micro-blogs. We first filtered messages based on syndrome keywords from the BioCaster Ontology, an extant knowledge model of laymen's terms. We then filtered the messages according to semantic features such as negation, hashtags, emoticons, humor and geography. The data covered 36 weeks for the US 2009 influenza season from 30th August 2009 to 8th May 2010. Results showed that our system achieved the highest Pearson correlation coefficient of 98.46% (p-value<;2.2e-16), an improvement of 3.98% over the previous state-of-the-art method. The results indicate that simple NLP-based enhancements to existing approaches to mine Twitter data can increase the value of this inexpensive resource.
{"title":"Enhancing Twitter Data Analysis with Simple Semantic Filtering: Example in Tracking Influenza-Like Illnesses","authors":"S. Doan, L. Ohno-Machado, Nigel Collier","doi":"10.1109/HISB.2012.21","DOIUrl":"https://doi.org/10.1109/HISB.2012.21","url":null,"abstract":"Systems that exploit publicly available user generated content such as Twitter messages have been successful in tracking seasonal influenza. We developed a novel filtering method for Influenza-Like-Ilnesses (ILI)-related messages using 587 million messages from Twitter micro-blogs. We first filtered messages based on syndrome keywords from the BioCaster Ontology, an extant knowledge model of laymen's terms. We then filtered the messages according to semantic features such as negation, hashtags, emoticons, humor and geography. The data covered 36 weeks for the US 2009 influenza season from 30th August 2009 to 8th May 2010. Results showed that our system achieved the highest Pearson correlation coefficient of 98.46% (p-value<;2.2e-16), an improvement of 3.98% over the previous state-of-the-art method. The results indicate that simple NLP-based enhancements to existing approaches to mine Twitter data can increase the value of this inexpensive resource.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114704401","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 past few years has witnessed rapid development in human genome research, in particular the genome-wide association studies (GWAS) and personalized medicine, which has been made possible by the advance in the Next Generation Sequencing (NGS) technologies that produces a large amount of sequencing data at an exceedingly low cost. New technologies for large-scale meta-analysis on genomic data continue to be developed, enabling the application of human genome research to clinical diagnosis and therapy, a trend dubbed “base pairs to bedside”. However, further progress in this area has been increasingly impeded by the constraints in accessing sequencing data, due in part to privacy concerns involved in data sharing. The current approach to protecting human genomic data is mainly based upon data-use agreements, which involves a time-consuming application/review/agreement process. To enable more convenient data access, this paper proposes a data analysis model that allows biomedical researchers and healthcare practitioners to use the sensitive genomic data that cannot be directly released in an efficient fashion, through the computing service over the data (instead of direct access to the data) provided by a large data center.
{"title":"Privacy Protection in Sharing Personal Genome Sequencing Data","authors":"Xiaofeng Wang, Haixu Tang","doi":"10.1109/HISB.2012.68","DOIUrl":"https://doi.org/10.1109/HISB.2012.68","url":null,"abstract":"The past few years has witnessed rapid development in human genome research, in particular the genome-wide association studies (GWAS) and personalized medicine, which has been made possible by the advance in the Next Generation Sequencing (NGS) technologies that produces a large amount of sequencing data at an exceedingly low cost. New technologies for large-scale meta-analysis on genomic data continue to be developed, enabling the application of human genome research to clinical diagnosis and therapy, a trend dubbed “base pairs to bedside”. However, further progress in this area has been increasingly impeded by the constraints in accessing sequencing data, due in part to privacy concerns involved in data sharing. The current approach to protecting human genomic data is mainly based upon data-use agreements, which involves a time-consuming application/review/agreement process. To enable more convenient data access, this paper proposes a data analysis model that allows biomedical researchers and healthcare practitioners to use the sensitive genomic data that cannot be directly released in an efficient fashion, through the computing service over the data (instead of direct access to the data) provided by a large data center.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116784120","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}
D. Mowery, Pamela W. Jordan, J. Wiebe, W. Chapman, Lin Liu
This pilot study aims to determine how well subjects annotate assertions about problem mentions in clinical text and determine if a statistical difference exists between subjects with and without clinical domain knowledge.
{"title":"Does Domain Knowledge Matter for Assertion Annotation in Clinical Texts?","authors":"D. Mowery, Pamela W. Jordan, J. Wiebe, W. Chapman, Lin Liu","doi":"10.1109/HISB.2012.61","DOIUrl":"https://doi.org/10.1109/HISB.2012.61","url":null,"abstract":"This pilot study aims to determine how well subjects annotate assertions about problem mentions in clinical text and determine if a statistical difference exists between subjects with and without clinical domain knowledge.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115355298","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 present a new automatic method for coronary artery vessel detection. We employ a texture modelling approach based on image textons as texture features, in the context of a classification experiment, where we attempt to discriminate between vessel and non-vessel like shapes in X-ray angiogram images. Experiments were conducted on a real patient database. The results show that the proposed model can perform well and distinguish vessel areas from others in an efficient manner, and outperforms other existing methods.
{"title":"Automatic Detection of Coronary Vessels Using Mutli-scale Texture Dictionaries","authors":"A. Zifan, B. Chapman","doi":"10.1109/HISB.2012.40","DOIUrl":"https://doi.org/10.1109/HISB.2012.40","url":null,"abstract":"In this paper we present a new automatic method for coronary artery vessel detection. We employ a texture modelling approach based on image textons as texture features, in the context of a classification experiment, where we attempt to discriminate between vessel and non-vessel like shapes in X-ray angiogram images. Experiments were conducted on a real patient database. The results show that the proposed model can perform well and distinguish vessel areas from others in an efficient manner, and outperforms other existing methods.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"161 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129087370","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}
B. Chapman, Mona Wong, Claudiu Farcas, P. Reynolds
We describe a web-based, volumetric image annotation tool that is based entirely on HTML5/CSS3 web presentation technologies. The annotation tool can be used on a wide variety of volumetric medical image formats. The application interfaces with ontology web services so that the annotations are labeled with well-defined terms.
{"title":"Annio: A Web-Based Tool for Annotating Medical Images with Ontologies","authors":"B. Chapman, Mona Wong, Claudiu Farcas, P. Reynolds","doi":"10.1109/HISB.2012.72","DOIUrl":"https://doi.org/10.1109/HISB.2012.72","url":null,"abstract":"We describe a web-based, volumetric image annotation tool that is based entirely on HTML5/CSS3 web presentation technologies. The annotation tool can be used on a wide variety of volumetric medical image formats. The application interfaces with ontology web services so that the annotations are labeled with well-defined terms.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014325","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}
Pharmaceutical drugs prescribed for the prevention, treatment or cure of diseases can have adverse reactions or side-effects that lead to further health complications or sometimes even death. Most of the common side-effects of drugs, reported by their manufacturer, are based on clinical trials. However, not all possible side-effects are identified, as their detection is limited by the extent of the number and diversity of the participants in the trials. Online medical help forums where patients voluntarily provide feedback on the drugs they take, provide an excellent source for identifying the unreported side-effects of drugs. Mining for these side-effects would help patients make informed decisions about the suitability of a drug for their treatment and also for health authorities to take appropriate action against drug manufacturers. In this paper we present a Hidden Markov Model based text mining system that can be used to extract adverse side-effects of drugs from online medical forums.
{"title":"Mining Adverse Drug Side-Effects from Online Medical Forums","authors":"Hariprasad Sampathkumar, Bo Luo, Xue-wen Chen","doi":"10.1109/HISB.2012.75","DOIUrl":"https://doi.org/10.1109/HISB.2012.75","url":null,"abstract":"Pharmaceutical drugs prescribed for the prevention, treatment or cure of diseases can have adverse reactions or side-effects that lead to further health complications or sometimes even death. Most of the common side-effects of drugs, reported by their manufacturer, are based on clinical trials. However, not all possible side-effects are identified, as their detection is limited by the extent of the number and diversity of the participants in the trials. Online medical help forums where patients voluntarily provide feedback on the drugs they take, provide an excellent source for identifying the unreported side-effects of drugs. Mining for these side-effects would help patients make informed decisions about the suitability of a drug for their treatment and also for health authorities to take appropriate action against drug manufacturers. In this paper we present a Hidden Markov Model based text mining system that can be used to extract adverse side-effects of drugs from online medical forums.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115763919","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}
Benjamin M. Good, Salvatore Loguercio, Max Nanis, A. Su
Games are emerging as a powerful organizational and motivational tactic throughout many areas of society. Wherever people have a goal that they are having trouble reaching, be it getting their chores done [1], learning all the functions of Microsoft Visual studio [2], or finishing a 10K [3], many are finding success by posing the required tasks as elements of games. Games can turn small units of work, that alone might seem boring, into fun steps taken towards a meaningful success. In doing so, they can sometimes dramatically increase individuals' chances of reaching their objectives. The process of translating elements of non-game contexts (e.g. science, most traditional work, learning, exercise, etc.) into aspects of games is now known as `gamification'.
{"title":"genegames.org: High-Throughput Access to Biological Knowledge and Reasoning through Online Games","authors":"Benjamin M. Good, Salvatore Loguercio, Max Nanis, A. Su","doi":"10.1109/HISB.2012.70","DOIUrl":"https://doi.org/10.1109/HISB.2012.70","url":null,"abstract":"Games are emerging as a powerful organizational and motivational tactic throughout many areas of society. Wherever people have a goal that they are having trouble reaching, be it getting their chores done [1], learning all the functions of Microsoft Visual studio [2], or finishing a 10K [3], many are finding success by posing the required tasks as elements of games. Games can turn small units of work, that alone might seem boring, into fun steps taken towards a meaningful success. In doing so, they can sometimes dramatically increase individuals' chances of reaching their objectives. The process of translating elements of non-game contexts (e.g. science, most traditional work, learning, exercise, etc.) into aspects of games is now known as `gamification'.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126095039","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}
M. Rahman, D. You, Matthew S. Simpson, Sameer Kiran Antani, Dina Demner-Fushman, G. Thoma
This paper presents an interactive biomedical image retrieval system based on automatic visual region-of-interest (ROI) extraction and classification into visual concepts. In biomedical articles, authors often use annotation markers such as arrows, letters or symbols overlaid on figures and illustrations in the articles to highlight ROIs. These annotations are then referenced and correlated with concepts in the caption text or figure citations in the article text. This association creates a bridge between the visual characteristics of important regions within an image and their semantic interpretation. Our proposed method at first localizes and recognizes the annotations by utilizing a combination of rule-based and statistical image processing techniques. Identifying these assists in extracting ROIs that are likely to be highly relevant to the discussion in the article text. The image regions are then annotated for classification using biomedical concepts obtained from a glossary of imaging terms. Similar automatic ROI extraction can be applied to query images, or user may interactively mark an ROI. As a result of our method, visual characteristics of the ROIs can be mapped to text concepts and then used to search image captions. In addition, the system can toggle the search process from purely visual to a textual one (cross-modal) or integrate both visual and textual search in a single process (multi-modal) based on utilizing user feedback. The hypothesis, that such approaches would improve biomedical image retrieval, is validated through experiments on a biomedical article dataset of thoracic CT scans from the collection of ImageCLEF'2010 medical retrieval track.
{"title":"An Interactive Image Retrieval Framework for Biomedical Articles Based on Visual Region-of- Interest (ROI) Identification and Classification","authors":"M. Rahman, D. You, Matthew S. Simpson, Sameer Kiran Antani, Dina Demner-Fushman, G. Thoma","doi":"10.1109/HISB.2012.18","DOIUrl":"https://doi.org/10.1109/HISB.2012.18","url":null,"abstract":"This paper presents an interactive biomedical image retrieval system based on automatic visual region-of-interest (ROI) extraction and classification into visual concepts. In biomedical articles, authors often use annotation markers such as arrows, letters or symbols overlaid on figures and illustrations in the articles to highlight ROIs. These annotations are then referenced and correlated with concepts in the caption text or figure citations in the article text. This association creates a bridge between the visual characteristics of important regions within an image and their semantic interpretation. Our proposed method at first localizes and recognizes the annotations by utilizing a combination of rule-based and statistical image processing techniques. Identifying these assists in extracting ROIs that are likely to be highly relevant to the discussion in the article text. The image regions are then annotated for classification using biomedical concepts obtained from a glossary of imaging terms. Similar automatic ROI extraction can be applied to query images, or user may interactively mark an ROI. As a result of our method, visual characteristics of the ROIs can be mapped to text concepts and then used to search image captions. In addition, the system can toggle the search process from purely visual to a textual one (cross-modal) or integrate both visual and textual search in a single process (multi-modal) based on utilizing user feedback. The hypothesis, that such approaches would improve biomedical image retrieval, is validated through experiments on a biomedical article dataset of thoracic CT scans from the collection of ImageCLEF'2010 medical retrieval track.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125173697","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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