2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)最新文献_第2页

Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver 用于人类肝脏肿瘤进展检测、识别和预测的智能图像处理技术

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007830

Liaqat Ali, A. Hussain, Jingpeng Li, A. A. Shah, Unnam Sudhakar, M. Mahmud, U. Zakir, X. Yan, B. Luo, M. Rajak

{"title":"Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver","authors":"Liaqat Ali, A. Hussain, Jingpeng Li, A. A. Shah, Unnam Sudhakar, M. Mahmud, U. Zakir, X. Yan, B. Luo, M. Rajak","doi":"10.1109/CICARE.2014.7007830","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007830","url":null,"abstract":"Clinical Decision Support (CDS) aids in early diagnosis of liver cancer, a potentially fatal disease prevalent in both developed and developing countries. Our research aims to develop a robust and intelligent clinical decision support framework for disease management of cancer based on legacy Ultrasound (US) image data collected during various stages of liver cancer. The proposed intelligent CDS framework will automate real-time image enhancement, segmentation, disease classification and progression in order to enable efficient diagnosis of cancer patients at early stages. The CDS framework is inspired by the human interpretation of US images from the image acquisition stage to cancer progression prediction. Specifically, the proposed framework is composed of a number of stages where images are first acquired from an imaging source and pre-processed before running through an image enhancement algorithm. The detection of cancer and its segmentation is considered as the second stage in which different image segmentation techniques are utilized to partition and extract objects from the enhanced image. The third stage involves disease classification of segmented objects, in which the meanings of an investigated object are matched with the disease dictionary defined by physicians and radiologists. In the final stage; cancer progression, an array of US images is used to evaluate and predict the future stages of the disease. For experiment purposes, we applied the framework and classifiers to liver cancer dataset for 200 patients. Class distributions are 120 benign and 80 malignant in this dataset.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122516457","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

New frequent pattern mining algorithm tested for activities models creation 新的频繁模式挖掘算法测试了活动模型的创建

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007836

Mohamed Tarik Moutacalli, A. Bouzouane, B. Bouchard

引用次数: 6

The design, implementation and evaluation of a relaxation service with facial emotion detection 基于面部情绪检测的放松服务的设计、实现与评价

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007832

S. Tivatansakul, M. Ohkura

引用次数: 5

An efficient Computer Aided Decision Support System for breast cancer diagnosis using Echo State Network classifier 基于回声状态网络分类器的高效乳腺癌诊断计算机辅助决策支持系统

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007829

S. Wajid, A. Hussain, B. Luo

引用次数: 8

Developing an affective Point-of-Care technology 开发一种有效的即时护理技术

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007837

Pedro H. F. Bacchini, E. C. Lopes, Marco Aurelio G. de A. Barbosa, J. O. Ferreira, O. C. S. Neto, A. Rocha, T. M. D. A. Barbosa

{"title":"Developing an affective Point-of-Care technology","authors":"Pedro H. F. Bacchini, E. C. Lopes, Marco Aurelio G. de A. Barbosa, J. O. Ferreira, O. C. S. Neto, A. Rocha, T. M. D. A. Barbosa","doi":"10.1109/CICARE.2014.7007837","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007837","url":null,"abstract":"Mobile intelligent clinical monitoring systems provide mobility and out of hospital monitoring. It can be used in the follow-up of high-risk patients in out of hospital situations and to monitor “healthy” persons to prevent medical events. The inherent characteristics of local diagnosis and actuation permit an improvement and advance in the diagnosis and emergency decision support. Additionally, Affective Systems have been used in different applications, such as stress monitoring in aircraft seats and managing sensitivity in autism spectrum disorder. Although many scientific progresses have been made there are many computational challenges in order to embedded affectivity into traditional user interfaces. For example, context-sensitive algorithms, low-complexity pattern recognition models and hardware customizations are requirements to support the simplification of user's experience becoming more intuitive, transparent and less obstructive. In this paper a multiparametric affective monitor is presented. The Emopad acquisition system has been developed to analyze user's biofeedback particularly when they are playing games. It is able to capture Galvanic Skin Response (GSR), Temperature, Force, Heart Rate (HR) and its variability (HRV) while complementary algorithms are executed to recognize events related to user's emotional states. Also, in this paper a sliding window-based algorithm is presented and evaluated to detect specific events related to emotional responses. The success of multiparametric affective monitors can lead to a paradigm shift, establishing new scenarios for the Point-of-Care technologies applications.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"397 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121794636","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: 5

How to find your appropriate doctor: An integrated recommendation framework in big data context 如何找到适合自己的医生:大数据背景下的综合推荐框架

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007848

Hongxun Jiang, Wei Xu

{"title":"How to find your appropriate doctor: An integrated recommendation framework in big data context","authors":"Hongxun Jiang, Wei Xu","doi":"10.1109/CICARE.2014.7007848","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007848","url":null,"abstract":"To find a specialty-counterpart, diagnosis-accurate, skill-superb, reputation-high, and meanwhile cost-effective and distance-close doctor is always essential for patients but not an easy job. According to various categories of medical professions, the diversity of user symptoms, and the information asymmetry and incompetence of doctors' profiles as well as patients' medical history, today most recommender applications are difficult to fit this field. The emerging web medical databases and online communities, providing doctors information and user reviews for them respectively, make it possible to personalized medical recommender services. In this paper, we describe an integrated recommender framework for seeking doctors in accordance with patients' demand characteristics, including their illness symptoms and their preference. In the proposed method, a users' matching model is firstly suggested for finding the similarities between users' consultation and doctors' profiles. Second, to measure doctors' quality, doctors' experiences and dynamic user's opinions are considered. Finally, to combine the results of the relevance model and the quality model, an AHP based integrated method is suggested for doctor recommendation. A mobile recommender APP is proposed to demonstrate the framework as above. And a survey is carried out for method evaluation. The results illustrate the new recommender outperforms others on accuracy and efficiency, as well as user experience. Our paper provides an efficient method for doctor recommendation, which has good practical value in China regarding to its huge land area with medical resource's uneven distribution.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128254493","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: 25

Adaptive Splitting and Selection ensemble for breast cancer malignancy grading 用于乳腺癌恶性分级的自适应分裂和选择集合

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007841

B. Krawczyk, Lukasz Jelen, Michal Wozniak

{"title":"Adaptive Splitting and Selection ensemble for breast cancer malignancy grading","authors":"B. Krawczyk, Lukasz Jelen, Michal Wozniak","doi":"10.1109/CICARE.2014.7007841","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007841","url":null,"abstract":"The article presents an application of Adaptive Splitting and Selection (AdaSS) ensemble classifier in a real-life task of designing an efficient clinical decision support system for breast cancer malignancy grading. We approach the problem of cancer detection form a different angle - we already know that a given patient has a malignant type of cancer and we want to asses the level of that malignancy to propose the most efficient treatment. We carry a cytological image segmentation process with fuzzy c-means procedure and extract a set of highly discriminative features. However, the difficulty lies in the fact, that we have a high disproportion in the number of patients between the groups, which leads to an imbalanced classification problem. To address this, we propose to use a dedicated ensemble model, which is able to exploit local areas of competence in the decision space. AdaSS is a hybrid combined classifier, based on an evolutionary splitting of object space into clusters and simultaneous selection of most competent classifiers for each of them. To increase the overall accuracy of the classification, in the hybrid training algorithm of AdaSS we embedded a feature selection and trained weighted fusion of individual classifiers based on their support functions. Experimental investigation proves that the introduced method is more accurate than previously used classification approaches.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124846215","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: 1

An intelligent system for assisting family caregivers of dementia people 一种智能系统，用于协助痴呆症患者的家庭照顾者

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007838

V. Moshnyaga, Tanaka Osamu, Toshin Ryu, Akira Hayashida, D. Sakamoto, Yukimuchi Imai, Takuma Shibata

引用次数: 9

Intelligent emotions stabilization system using standardized images, breath sensor and biofeedback - new concept 智能情绪稳定系统使用标准化图像，呼吸传感器和生物反馈-新概念

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007833

O. Sokolov, Krzysztof Dobosz, J. Dreszer, B. Bałaj, Wlodzislaw Duch, Slawomir Grzelak, Tomasz Komendziński, D. Mikołajewski, Tomasz Piotrowski, M. Swierkocka, Piotr Weber

引用次数: 1

Rule based realtime motion assessment for rehabilitation exercises 基于规则的康复训练实时运动评估

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007845

Wenbing Zhao, Roanna Lun, D. Espy, M. A. Reinthal

{"title":"Rule based realtime motion assessment for rehabilitation exercises","authors":"Wenbing Zhao, Roanna Lun, D. Espy, M. A. Reinthal","doi":"10.1109/CICARE.2014.7007845","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007845","url":null,"abstract":"In this paper, we describe a rule based approach to realtime motion assessment of rehabilitation exercises. We use three types of rules to define each exercise: (1) dynamic rules, with each rule specifying a sequence of monotonic segments of the moving joint or body segment, (2) static rules for stationary joints or body segments, and (3) invariance rules that dictate the requirements of moving joints or body segments. A finite state machine based approach is used in dynamic rule specification and realtime assessment. In addition to the typical advantages of the rule based approach, such as realtime motion assessment with specific feedback, our approach has the following advantages: (1) increased reusability of the defined rules as well as the rule assessment engine facilitated by a set of generic rule elements; (2) increased customizability of the rules for each exercise enabled by the use of a set of generic rule elements and the use of extensible rule encoding method; and (3) increased robustness without relying on expensive statistical algorithms to tolerate motion sensing errors and subtle patient errors.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132293508","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: 51