Myung-kyung Suh, Jonathan Woodbridge, Tannaz Moin, M. Lan, N. Alshurafa, Lauren Samy, B. Mortazavi, Hassan Ghasemzadeh, A. Bui, Sheila Ahmadi, M. Sarrafzadeh
Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.
{"title":"Dynamic Task Optimization in Remote Diabetes Monitoring Systems","authors":"Myung-kyung Suh, Jonathan Woodbridge, Tannaz Moin, M. Lan, N. Alshurafa, Lauren Samy, B. Mortazavi, Hassan Ghasemzadeh, A. Bui, Sheila Ahmadi, M. Sarrafzadeh","doi":"10.1109/HISB.2012.10","DOIUrl":"https://doi.org/10.1109/HISB.2012.10","url":null,"abstract":"Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121325203","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}
K. Wagholikar, S. Sohn, Stephen T Wu, V. Kaggal, Sheila Buehler, R. Greenes, Tsung-Teh Wu, D. Larson, Hongfang Liu, Rajeev Chaudhry, L. Boardman
Colorectal cancer is the second leading cause of cancer-related deaths in the United States. However, 41% of patients do not receive adequate screening, since the surveillance guidelines for colonoscopy are complex and are not easily recalled by health care providers. As a potential solution, we developed a guideline based clinical decision support system (CDSS) that can interpret relevant freetext reports including indications, pathology and procedure notes. The CDSS was evaluated by comparing its recommendations with those of a gastroenterologist for a test set of 53 patients. The CDSS made the optimal recommendation in 48 cases, and helped the gastroenterologist revise the recommendation in 3 cases. We performed an error analysis for the 5 failure cases, and subsequently were able to modify the CDSS to output the correct recommendation for all the test cases. Results indicate that the system has a high potential for clinical deployment, but further evaluation and optimization is required. Limitations of our study are that the study was conducted at a single institution and with a single expert, and the evaluation did not include rare decision scenarios. Overall our work demonstrates the utility of natural language processing to enhance clinical decision support.
{"title":"Clinical Decision Support for Colonoscopy Surveillance Using Natural Language Processing","authors":"K. Wagholikar, S. Sohn, Stephen T Wu, V. Kaggal, Sheila Buehler, R. Greenes, Tsung-Teh Wu, D. Larson, Hongfang Liu, Rajeev Chaudhry, L. Boardman","doi":"10.1109/HISB.2012.11","DOIUrl":"https://doi.org/10.1109/HISB.2012.11","url":null,"abstract":"Colorectal cancer is the second leading cause of cancer-related deaths in the United States. However, 41% of patients do not receive adequate screening, since the surveillance guidelines for colonoscopy are complex and are not easily recalled by health care providers. As a potential solution, we developed a guideline based clinical decision support system (CDSS) that can interpret relevant freetext reports including indications, pathology and procedure notes. The CDSS was evaluated by comparing its recommendations with those of a gastroenterologist for a test set of 53 patients. The CDSS made the optimal recommendation in 48 cases, and helped the gastroenterologist revise the recommendation in 3 cases. We performed an error analysis for the 5 failure cases, and subsequently were able to modify the CDSS to output the correct recommendation for all the test cases. Results indicate that the system has a high potential for clinical deployment, but further evaluation and optimization is required. Limitations of our study are that the study was conducted at a single institution and with a single expert, and the evaluation did not include rare decision scenarios. Overall our work demonstrates the utility of natural language processing to enhance clinical decision support.","PeriodicalId":375089,"journal":{"name":"2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121486184","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}
Jyh-Ying Peng, Yen-Jen Chen, Marc D. Green, S. Forsburg, Chun-Nan Hsu
Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo [1]. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. The first step requires robust segmentation of the cell and the most distinguishable compartments (the nucleus) from images with varying focus conditions and qualities. We developed a segmentation system that can segment transmitted illumination images with focus gradient and varying contrast, and extract cell and nucleus boundaries. Global and locally adaptive corrections for focus gradient are applied to the image to accurately detect cell membrane and cytoplasm pixels. We use the gradient vector flow snake model [2] to segment individual cells, using a novel edge map based on detected cell membrane. We applied our system to multi-channel images of S. pombe, the whole data set contains about 4000 mutant genotypes each with at least three sets of transmitted illumination (bright field), Rad52-YFP and RPA-CFP images. Our system is able to correctly segment a majority of nuclei and cells in almost all images of sufficient quality, and performance is consistent over a wide variety of focus distance, field brightness, relative contrast and phenotypic characteristics. A quantitative evaluation is also performed using a set of hand produced gold standard segmentations of pombe cells, representing different image acquisition conditions and quality. We evaluated the percentage of cells detected, the accuracy of the final snake contours. The whole set of 60 gold standard images contain a total of 14,926 pombe cells, averaging about 249 cells per image, of which 97.5% were detected by nucleus segmentation and pixel classification of cell interior, and 89.0% were accurately segmented (defined as less than 10% pixel mismatch). Our system generated a total of 16,631 snake contours, of which 88.3% are true positives, the rest being false detections, incorrect merging or partial segmentation. After erroneous cell contours are removed by an automatic contour validation classifier, the remaining cell contours contain 98.3% true positives, this shows that although our system has a modest segmentation accuracy, the final cell contours generated is very reliable overall. For large scale high-throughput applications with huge amounts of data, in order to minimize the need for human intervention, the high reliability and robustness achieved by our system is very valuable. We have also compared with recent methods [3], and our method. In conclusion we have developed a multi-channel cell and nucleus segmentation system for S. pombe cells that uses nucleus protein fluorescence to correct for varying focus and contrast in the transmitted illumination image, combined with active contour segmentation and robust
{"title":"Simultaneous Segmentation of Cell and Nucleus in Schizosaccharomyces pombe Images with Focus Gradient","authors":"Jyh-Ying Peng, Yen-Jen Chen, Marc D. Green, S. Forsburg, Chun-Nan Hsu","doi":"10.1109/HISB.2012.41","DOIUrl":"https://doi.org/10.1109/HISB.2012.41","url":null,"abstract":"Schizosaccharomyces pombe shares many genes and proteins with humans and is a good model for chromosome behavior and DNA dynamics, which can be analyzed by visualizing the behavior of fluorescently tagged proteins in vivo [1]. However, performing a genome-wide screen for changes in such proteins requires developing methods that automate analysis of multiple images. The first step requires robust segmentation of the cell and the most distinguishable compartments (the nucleus) from images with varying focus conditions and qualities. We developed a segmentation system that can segment transmitted illumination images with focus gradient and varying contrast, and extract cell and nucleus boundaries. Global and locally adaptive corrections for focus gradient are applied to the image to accurately detect cell membrane and cytoplasm pixels. We use the gradient vector flow snake model [2] to segment individual cells, using a novel edge map based on detected cell membrane. We applied our system to multi-channel images of S. pombe, the whole data set contains about 4000 mutant genotypes each with at least three sets of transmitted illumination (bright field), Rad52-YFP and RPA-CFP images. Our system is able to correctly segment a majority of nuclei and cells in almost all images of sufficient quality, and performance is consistent over a wide variety of focus distance, field brightness, relative contrast and phenotypic characteristics. A quantitative evaluation is also performed using a set of hand produced gold standard segmentations of pombe cells, representing different image acquisition conditions and quality. We evaluated the percentage of cells detected, the accuracy of the final snake contours. The whole set of 60 gold standard images contain a total of 14,926 pombe cells, averaging about 249 cells per image, of which 97.5% were detected by nucleus segmentation and pixel classification of cell interior, and 89.0% were accurately segmented (defined as less than 10% pixel mismatch). Our system generated a total of 16,631 snake contours, of which 88.3% are true positives, the rest being false detections, incorrect merging or partial segmentation. After erroneous cell contours are removed by an automatic contour validation classifier, the remaining cell contours contain 98.3% true positives, this shows that although our system has a modest segmentation accuracy, the final cell contours generated is very reliable overall. For large scale high-throughput applications with huge amounts of data, in order to minimize the need for human intervention, the high reliability and robustness achieved by our system is very valuable. We have also compared with recent methods [3], and our method. In conclusion we have developed a multi-channel cell and nucleus segmentation system for S. pombe cells that uses nucleus protein fluorescence to correct for varying focus and contrast in the transmitted illumination image, combined with active contour segmentation and robust ","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":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123656910","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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