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

{"title":"An approximate inverse recipe method with application to automatic food analysis","authors":"Jieun Kim, M. Boutin","doi":"10.1109/CICARE.2014.7007831","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007831","url":null,"abstract":"We propose a method for automatically determining the amount of each ingredient used to prepare a commercial food using the information provided on its label. The method applies when no part of any ingredient is removed in the preparation process and as long as we can collect the nutrition data (e.g., from the USDA Food Database) for at least some of the ingredients. Using this information, we first find a set of initial minimum and maximum bounds for each ingredient amount. Then we improve these maximum and minimum bounds using an iterative method. The resulting bounds on the ingredient amounts can then be used to estimate the nutrient content of the food. We tested this approach for estimating the phenylalanine content of various commercial foods. Phenylalanine is an amino acid that must be carefully monitored when treating patients with the metabolic disease phenylketonuria (PKU). Our numerical tests indicate that the accuracy of our method is within an acceptable range (10mg Phe) for most of the foods we considered. We implemented a web-based application of our proposed method for public use. Our method should be applicable to the estimation of nutrients involved in the management of other medical diets.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"29 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":"129179737","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}

{"title":"Analysis of three-dimensional vasculature using multifractal theory","authors":"W. Ward, Yuchun Ding, L. Bai","doi":"10.1109/CICARE.2014.7007835","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007835","url":null,"abstract":"This paper investigates the use of multifractal formalism for characterising 3D brain vasculature of 2 different mammalian species. Multifractal properties were found across all the 3D vascular models. Variations in the analysis results appear to correspond with vessel density ans morphology. The implication of the research is that multifractal analysis could potentially provide a useful tool for clinical assessment of diseases that are known to alter density and structure of brain microvasculature.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"6 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":"115479013","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}

{"title":"Automating assessment in video game teletherapy: Data cutting","authors":"Martin Scott, W. Blewitt, G. Ushaw, J. Shi, G. Morgan, J. Eyre","doi":"10.1109/CICARE.2014.7007828","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007828","url":null,"abstract":"In this paper we describe how a video game designed to deliver a rehabilitation therapy can produce data of a standard that is clinically useful. Our approach is based entirely on commodity video game hardware, making our solution one that may be delivered in a cost efficient manner. The step of ensuring data fidelity was crucial in allowing clinical assessment to be derived from standard video game technology without therapist intervention. We achieved this by cutting the data to provide our statistical model with only the information that accurately represented patient activities that contribute to clinical assessment.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"29 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":"116337950","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}

{"title":"Exploring emotion in an e-learning system using eye tracking","authors":"Saromporn Charoenpit, M. Ohkura","doi":"10.1109/CICARE.2014.7007846","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007846","url":null,"abstract":"Since appropriate emotions are a sign of mental health, learners who have good mental health learn more successfully. In e-learning systems, emotions are critical for learners to create positive contexts for optimal learning. To data, however, few e-learning systems have derived emotions from eye tracking data. With eye tracking equipment, we recorded the eye movements of learners and calculated their eye metric indexes, which we focused on to explore their relationship to two learner emotions: interest and boredom. We designed and implemented a prototype and experimentally evaluated it.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"27 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":"122009390","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}

{"title":"A novel cardiovascular decision support framework for effective clinical risk assessment","authors":"Kamran Farooq, J. Karasek, H. Atassi, A. Hussain, Peipei Yang, C. Macrae, M. Mahmud, B. Luo, W. Slack","doi":"10.1109/CICARE.2014.7007843","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007843","url":null,"abstract":"The aim of this study is to help improve the diagnostic and performance capabilities of Rapid Access Chest Pain Clinics (RACPC), by reducing delay and inaccuracies in the cardiovascular risk assessment of patients with chest pain by helping clinicians effectively distinguish acute angina patients from those with other causes of chest pain. Key to our new approach is (1) an intelligent prospective clinical decision support framework for primary and secondary care clinicians, (2) learning from missing/impartial clinical data using Bernoulli mixture models and Expectation Maximisation (EM) techniques, (3) utilisation of state-of-the-art feature section, pattern recognition and data mining techniques for the development of intelligent risk prediction models for cardiovascular patients. The study cohort comprises of 632 patients suspected of cardiac chest pain. A retrospective data analysis of the clinical studies evaluating clinical risk factors for chest pain patients was performed for the development of RACPC specific risk assessment models to distinguish between cardiac and non cardiac chest pain. A comparative analysis case study of machine learning methods was carried out for predicting RACPC clinical outcomes using real patient data acquired from Raigmore Hospital in Inverness, UK. The proposed framework was also validated using the University of Cleveland's Heart Disease dataset which contains 76 attributes, but all published experiments refer to using a subset of 14 of them. Experiments with the Cleveland database (based on 18 clinical features of 270 patients) were concentrated on attempting to distinguish the presence of heart disease (values 1, 2, 3, 4) from absence (value 0). The new clinical models, having been evaluated in clinical practice, resulted in very good predictive power, demonstrating general performance improvement over benchmark multivariate statistical classifiers. As part of these case studies, various online RACPC risk assessment prototypes have been developed which are being deployed in the clinical setting (NHS Highland) for clinical trial purposes.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"137 8 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":"128710088","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}

{"title":"Towards a prototype medical system for devices vigilance and patient safety","authors":"Antonios Deligiannakis, Nikos Giatrakos, N. Pallikarakis","doi":"10.1109/CICARE.2014.7007852","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007852","url":null,"abstract":"For all healthcare institutions and organizations, patient safety is of the utmost importance. A factor that influences patient safety is the existence (or not) of observed adverse events associated with medical devices. Upon the detection of adverse events, all healthcare providers that own the affected medical devices should be promptly notified. In this paper we present the core of a prototype system for medical devices vigilance and patient safety. We present the architecture of this system, the way that it detects the healthcare providers that need to be notified through an entity matching algorithm, as well as briefly present its user interface.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"8 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":"114279083","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}

{"title":"A feasibility study of using a single Kinect sensor for rehabilitation exercises monitoring: A rule based approach","authors":"Wenbing Zhao, D. Espy, M. A. Reinthal, Hai Feng","doi":"10.1109/CICARE.2014.7007827","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007827","url":null,"abstract":"In this paper, we present a feasibility study for using a single Microsoft Kinect sensor to assess the quality of rehabilitation exercises. Unlike competing studies that have focused on the validation of the accuracy of Kinect motion sensing data at the level of joint positions, joint angles, and displacement of joints, we take a rule based approach. The advantage of our approach is that it provides a concrete context for judging the feasibility of using a single Kinect sensor for rehabilitation exercise monitoring. Our study aims to answer the following question: if it is found that Kinect's measurement on a metric deviates from the ground truth by some amount, is this an acceptable error? By defining a set of correctness rules for each exercise, the question will be answered definitively with no ambiguity. Defining appropriate context in a validation study is especially important because (1) the deviation of Kinect measurement from the ground truth varies significantly for different exercises, even for the same joint, and (2) different exercises have different tolerance levels for the movement restrictions of body segments. In this study, we also show that large but systematic deviations of the Kinect measurement from the ground truth are not as harmful as it seems because the problem can be overcome by adjusting parameters in the correctness rules.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"1 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":"129921029","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}

{"title":"A comparison of syntax, semantics, and pragmatics in spoken language among residents with Alzheimer's disease in managed-care facilities","authors":"C. Guinn, Ben Singer, A. Habash","doi":"10.1109/CICARE.2014.7007840","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007840","url":null,"abstract":"This research is a discriminative analysis of conversational dialogues involving individuals suffering from dementia of Alzheimer's type. Several metric analyses are applied to the transcripts of the Carolina Conversation Corpus in order to determine if there are significant statistical differences between individuals with and without Alzheimer's disease. Our prior research suggests that there exist measurable linguistic differences between managed-care residents diagnosed with Alzheimer's disease and their caregivers. This paper presents results comparing managed-care residents diagnosed with Alzheimer's disease to other managed-care residents. Results from the analysis indicate that part-of-speech and lexical richness statistics may not be good distinguishing attributes. However, go-ahead utterances and certain fluency measures provide defensible means of differentiating the linguistic characteristics of spontaneous speech between individuals that are and are not diagnosed with Alzheimer's disease. Two machine learning algorithms were able to classify the speech of individuals with and without dementia of the Alzheimer's type with accuracy up to 80%.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"124 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":"121532006","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}

{"title":"Patient stratification based on Activity of Daily Living score using Relational Self-Organizing Maps","authors":"Mohammad Khalilia, M. Popescu, J. Keller","doi":"10.1109/CICARE.2014.7007842","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007842","url":null,"abstract":"Stratification is a valuable technique for providing an insight on the structure of the patient population based on some features such as Activity of Daily Living (ADL) scores. Grouping patients can play an important role in designing clinical trials or improving care delivery. In this paper, we present a method for stratifying patients based on their ADL scores. Every patient is represented by a time series consisting of ADL scores recorded over a period of up to two years. This approach relies on Dynamic Time Warping (DTW) technique to measure the similarity between two time series and then using Relational Self-Organizing Maps (RSOM) to discover patient clusters. The analysis was performed on a population of 6,000 patients. Six clusters were discovered: patients with high risk and steady ADL trajectory, low risk and steady trajectory, patients with sudden ADL score jumps, patients with declining ADL score and others with steady inclining trajectory.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"56 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":"127019414","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}

{"title":"Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders","authors":"A. Gelzinis, A. Verikas, E. Vaičiukynas, M. Bacauskiene, J. Minelga, M. Hållander, V. Uloza, E. Padervinskis","doi":"10.1109/CICARE.2014.7007844","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007844","url":null,"abstract":"Exploration of various features and different structures of data dependent random forests in screening for laryngeal disorders through analysis of sustained phonation recorded by acoustic and contact microphones is the main objective of this study. To obtain a versatile characterization of voice samples, 14 different sets of features were extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We proposed a new, data dependent random forest-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest was also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the Perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the LP-coefficients and LPCT-coefficients feature sets exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for classification. The proposed data dependent random forest significantly outperformed traditional designs.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"217 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":"134367511","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}