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13th International Conference on Wearable Micro and Nano Technologies for Personalised Health (pHealth)

作者： Henriques, Jorge de Carvalho, Paulo Rocha, Teresa Paredes, Simao Morais, Joao Univ Coimbra Dept Informat Engn Ctr Informat & Syst Coimbra Portugal Polytech Inst Coimbra IPC ISEC Comp Sci & Syst Engn Dept Coimbra Portugal Leiria Hosp Ctr Dept Cardiol Leiria Portugal

ISBN: (纸本)9781614996538;9781614996521

The employment of personal health systems (pHealth) is a valuable concept in the management of chronic diseases, particularly in the context of cardiovascular diseases. By means of a continuous monitoring of the patient it is possible to seamless access multiple sources of data, including physiological signals, providing professionals with a global and reliable view of the patient's status. In practice, it is possible the prompt diagnosis of events, the early prediction of critical events and the implementation of personalized therapies. Furthermore, the information collected during long periods creates new opportunities in the diagnosis of a disease, in its evolution, and in the prediction of possible complications. The focus of this work is the research and implementation of multi-parametric algorithms for data analysis in pHealth context, including data mining techniques as well as physiological signal modelling and processing. In particular, fusion strategies for cardiovascular status evaluation (namely cardiovascular risk assessment and cardiac function estimation) and multi-parametric prediction algorithms for the early detection of cardiovascular events (such as hypertension, syncope and heart failure decompensation) will be addressed.

关键词： pHealth Bio-signals Intelligent data processing predictive algorithms

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Application of Function Points and Data Mining Techniques for Software Estimation - A Combined Approach 1

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25th International Workshop on Software Measurement / 10th International Conference on Software Process and Product Measurement (IWSM-Mensura)

作者： Pospieszny, Przemyslaw Czarnacka-Chrobot, Beata Kobylinski, Andrzej Warsaw Sch Econ Inst Informat Syst & Digital Econ PL-02513 Warsaw Poland

ISBN: (数字)9783319242859

ISBN: (纸本)9783319242859;9783319242842

Project estimation is recognized as one of the most challenging processes in software project management on which project success is dependable. Traditional estimation methods based on expert knowledge and analogy tend to be error prone and deliver overoptimistic assessments. Methods derived from function points are good sizing tools but do not reflect organizations' specific project management culture. Due to those deficiencies in recent years data mining techniques are explored as an alternative estimation method. The aim of this paper is to present a combined approach of functional sizing measurement and three data mining techniques for effort and duration estimation at project early stages: generalized linear models, artificial neural networks and CHAID decision trees. The estimation accuracy of these models is compared in order to determine their potential usefulness for deployment within organizations. Moreover a merged approach of combining algorithms' results is proposed in order to increase prediction accuracy and overcome possibility of overfitting occurrence.

关键词： Project estimation Effort and duration estimation Software measurement Functional size measurement Function points Data mining predictive algorithms

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Regression Based Prediction Algorithm for Remote Controlling of IoT Based Applications

Regression Based Prediction Algorithm for Remote Controlling...

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International Conference on Computing and Network Communications (CoCoNet)

作者： Wagle, Satyavrat Sathe, Tejas Vamburkar, Gandhar Gaikaiwari, Anand Vishwakarma Inst Technol Dept Instrumentat & Control Engn Pune Maharashtra India MAEERs MIT Dept Comp Sci Pune Maharashtra India

ISBN: (纸本)9781467373098

The growing paradigm of Internet of Things (IoT), has necessitated the development of new and improved monitoring and control methodologies for applications which were hitherto subjected to the constraints of a local control system. This paper proposes a predictive system to monitor and control embedded applications linked to IoT. A brief introduction of Internet of Things and its resultant effects on control system design are discussed followed by an overview of regression systems. Next, we present a prototype model implementing a regression based control system model, and the inferences from the data it has generated on execution. Finally, we discuss the possible modifications and application areas of regression based processes in IoT.

关键词： Internet of Things Artificial Intelligence predictive algorithms

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The Neurobiology of Executive Function Under Stress and Optimization of Performance 9th

The Neurobiology of Executive Function Under Stress and Opti...

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9th International Conference on Augmented Cognition (AC) Held as Part of 17th International Conference on Human-Computer Interaction (HCI International)

作者： Rasmusson, Ann M. Irvine, John M. VA Boston Healthcare Syst Boston MA 02130 USA Boston Univ Boston MA 02215 USA Draper Lab Cambridge MA USA

ISBN: (纸本)9783319208169;9783319208152

Much basic and clinical research to date has investigated predictors of stress resilience and vulnerability, indicating, for example, that broad impact neurobiological factors, such as neuropeptide Y (NPY) and neuroactive steroids, are mechanistically related to short term stress resilience, as well as longterm patterns of stress-related medical and neuropsychiatric comorbidities. The problem is that we lack good methods for identifying predictors of stress resilience or vulnerability at an individual level, so that human performance and therapeutic interventions can be targeted precisely to underlying points of malfunction for maximum effectiveness. We thus propose modified experimental designs that capitalize on our growing capacities to query and analyze multimodal data across the translational levels of human biology and behavior. We propose that use of these methods in studies of individuals participating in intense military training or returning from deployment could enable better prediction of performance, and development of more effective personalized interventions aimed at optimizing and maintaining stress resilience over time.

关键词： Resilience PTSD Translational neuroscience Neuropeptide Y Allopregnanolone Neuroactive steroids predictive algorithms Functional data analysis Non-linear modeling Machine learning

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Predicting Health Utilities for Children With Autism Spectrum Disorders

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AUTISM RESEARCH 2014年第6期7卷 649-663页

作者： Payakachat, Nalin Tilford, J. Mick Kuhlthau, Karen A. van Exel, N. Job Kovacs, Erica Bellando, Jayne Pyne, Jeffrey M. Brouwer, Werner B. F. Univ Arkansas Med Sci Div Pharmaceut Evaluat & Policy Little Rock AR 72223 USA Univ Arkansas Med Sci Dept Hlth Policy & Management Little Rock AR 72223 USA Univ Arkansas Med Sci Dept Pediat Little Rock AR 72223 USA Harvard Univ Sch Med Dept Pediat Boston MA 02115 USA Massachusetts Gen Hosp Ctr Adolescent Hlth Policy Boston MA 02114 USA Erasmus Univ Dept Hlth Policy & Management Rotterdam Netherlands Columbia Univ Dept Psychiat Med Ctr New York NY USA Cent Arkansas Vet Healthcare Syst Ctr Mental Healthcare & Outcomes Res Little Rock AR USA Univ Arkansas Med Sci Inst Psychiat Res Little Rock AR 72223 USA

Comparative effectiveness of interventions for children with autism spectrum disorders (ASDs) that incorporates costs is lacking due to the scarcity of information on health utility scores or preference-weighted outcomes typically used for calculating quality-adjusted life years (QALYs). This study created algorithms for mapping clinical and behavioral measures for children with ASDs to health utility scores. The algorithms could be useful for estimating the value of different interventions and treatments used in the care of children with ASDs. Participants were recruited from two Autism Treatment Network sites. Health utility data based on the Health Utilities Index Mark 3 (HUI3) for the child were obtained from the primary caregiver (proxy-reported) through a survey (N=224). During the initial clinic visit, proxy-reported measures of the Child Behavior Checklist, Vineland II Adaptive Behavior Scales, and the Pediatric Quality of Life Inventory 4.0 (start measures) were obtained and then merged with the survey data. Nine mapping algorithms were developed using the HUI3 scores as dependent variables in ordinary least squares regressions along with the start measures, the Autism Diagnostic Observation Schedule, to measure severity, child age, and cognitive ability as independent predictors. In-sample cross-validation was conducted to evaluate predictive accuracy. Multiple imputation techniques were used for missing data. The average age for children with ASDs in this study was 8.4 (standard deviation=3.5) years. Almost half of the children (47%) had cognitive impairment (IQ70). Total scores for all of the outcome measures were significantly associated with the HUI3 score. The algorithms can be applied to clinical studies containing start measures of children with ASDs to predict QALYs gained from interventions. (c) 2014 International Society for Autism Research, Wiley Periodicals, Inc.

关键词： mapping predictive algorithms equating measure autism health utilities clinical measure behavioral measure quality of life measure

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Autoimmune responses in T1DM: quantitative methods to understand onset, progression, and prevention of disease

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PEDIATRIC DIABETES 2014年第3期15卷 162-174页

作者： Jaberi-Douraki, Majid Liu, Shang Wan (Shalon) Pietropaolo, Massimo Khadra, Anmar McGill Univ Dept Physiol Montreal PQ H3G 1Y6 Canada Univ Michigan Immunogenet Lab Ann Arbor MI 48109 USA

Understanding the physiological processes that underlie autoimmune disorders and identifying biomarkers to predict their onset are two pressing issues that need to be thoroughly sorted out by careful thought when analyzing these diseases. Type 1 diabetes (T1D) is a typical example of such diseases. It is mediated by autoreactive cytotoxic CD4(+) and CD8(+) T-cells that infiltrate the pancreatic islets of Langerhans and destroy insulin-secreting -cells, leading to abnormal levels of glucose in affected individuals. The disease is also associated with a series of islet-specific autoantibodies that appear in high-risk subjects (HRS) several years prior to the onset of diabetes-related symptoms. It has been suggested that T1D is relapsing-remitting in nature and that islet-specific autoantibodies released by lymphocytic B-cells are detectable at different stages of the disease, depending on their binding affinity (the higher, the earlier they appear). The multifaceted nature of this disease and its intrinsic complexity make this disease very difficult to analyze experimentally as a whole. The use of quantitative methods, in the form of mathematical models and computational tools, to examine the disease has been a very powerful tool in providing predictions and insights about the underlying mechanism(s) regulating its onset and development. Furthermore, the models developed may have prognostic implications by aiding in the enrollment of HRS into trials for T1D prevention. In this review, we summarize recent advances made in determining T- and B-cell involvement in T1D using these quantitative approaches and delineate areas where mathematical modeling can make further contributions in unraveling certain aspect of this disease.

关键词： autoantibodies autoimmunity avidity B-cells Markov models mathematical models predictive algorithms T-cells T1D -cells

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Prognostic significance of immunohistochemistry-based markers and algorithms in immunochemotherapy-treated diffuse large B cell lymphoma patients

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HISTOPATHOLOGY 2013年第6期63卷 788-801页

作者： Culpin, Rachel E. Sieniawski, Michal Angus, Brian Menon, Geetha K. Proctor, Stephen J. Milne, Paul McCabe, Kate Mainou-Fowler, Tryfonia Newcastle Univ Northern Inst Canc Res Newcastle Upon Tyne NE2 4HH Tyne & Wear England Royal Victoria Infirm Newcastle Upon Tyne NE1 4LP Tyne & Wear England

AimsTo reassess the prognostic validity of immunohistochemical markers and algorithms identified in the CHOP era in immunochemotherapy-treated diffuse large B cell lymphoma patients. Methods and resultsThe prognostic significance of immunohistochemical markers (CD10, Bcl-6, Bcl-2, MUM1, Ki-67, CD5, GCET1, FoxP1, LMO2) and algorithms (Hans, Hans*, Muris, Choi, Choi*, Nyman, Visco-Young, Tally) was assessed using clinical diagnostic blocks taken from an unselected, population-based cohort of 190 patients treated with R-CHOP. Dichotomizing expression, low CD10 (<10%), low LMO2 (<70%) or high Bcl-2 (80%) predicted shorter overall survival (OS;P=0.033, P=0.010 and P=0.008, respectively). High Bcl-2 (80%), low Bcl-6 (<60%), low GCET1 (<20%) or low LMO2 (<70%) predicted shorter progression-free survival (PFS;P=0.001, P=0.048, P=0.045 and P=0.002, respectively). The Hans, Hans* and Muris classifiers predicted OS (P=0.022, P=0.037 and P=0.011) and PFS (P=0.021, P=0.020 and P=0.004). The Choi, Choi* and Tally were associated with PFS (P=0.049, P=0.009 and P=0.023). In multivariate analysis, the International Prognostic Index (IPI) was the only independent predictor of outcome (OS;HR: 2.60, P<0.001 and PFS;HR: 2.91, P<0.001). ConclusionsResults highlight the controversy surrounding immunohistochemistry-based algorithms in the R-CHOP era. The need for more robust markers, applicable to the clinic, for incorporation into improved prognostic systems is emphasized.

关键词： diffuse large B cell lymphoma immunohistochemistry N-HL predictive algorithms prognosis survival

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Development of CO₂ emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2012年第8期34卷 974-982页

作者： Pathmanathan, Elangeshwaran Ibrahim, Rosdiazli Asirvadam, Vijanth Sagayan Univ Teknol PETRONAS Dept Elect & Elect Engn Tronoh 31720 Perak Malaysia

This paper aims to develop a CO2 emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression (LS-SVR). The Malaysia Department of Environment is actively imposing the Clean Air Regulation to mandate heavy industries to comply with emission limits. One of the latest measures is to mandate the installation of analytical instrumentation known as a continuous emission monitoring system (CEMS) to report the emission level online to the Department of Environment office. As a hardware-based analyser, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques are often preferred and considered a feasible alternative to replace the CEMS for regulatory compliance. The LS-SVR model is built based on the emissions from an acid gas incinerator that operates in a liquefied natural gas complex. Simulated annealing is first used to determine the initial hyper-parameters, which are further optimized based on the performance of the model using a Nelder-Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on back-propagation neural networks in both training and testing data.

关键词： Artificial neural networks industrial pollution predictive algorithms support vector machines

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Current status of clopidogrel pharmacogenomics

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PHARMACOGENOMICS 2012年第15期13卷 1671-1674页

作者： Giusti, Betti Gori, Anna Maria Marcucci, Rossella Abbate, Rosanna Univ Florence Careggi Hosp Dept Med Surg & Crit Care Atherothrombot Dis Ctr I-50134 Florence Italy Don Carlo Gnocchi Fdn Inst Sci Florence Italy

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CO₂ Emission Model Development Employing Particle Swarm Optimized - Least Squared SVR (PSO-LSSVR) Hybrid Algorithm

CO<sub>2</sub> Emission Model Development Employing Particle...

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4th International Conference on Intelligent and Advanced Systems (ICIAS) and A Conference of World Engineering, Science and Technology Congress (ESTCON)

作者： Pathmanathan, Elangeshwaran Ibrahim, Rosdiazli Asirvadam, Vijanth Sagayan Univ Teknol PETRONAS Dept Elect & Elect Engn Tronoh 31720 Perak Malaysia

ISBN: (纸本)9781457719677

This paper aims to develop a CO2 emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS). CEMS is used to report emission level online to DOE office. As hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LSSVR model is developed based on the emissions data from an acid gas incinerator that operates in a LNG Complex. PSO technique is used to optimize the hyperparameters used in training the LSSVR model. Overall, the LSSVR models have shown good performance in certain key areas in comparison with the BPNN model.

关键词： artificial neural networks industrial pollution particle swarm optimization predictive algorithms support vector machines

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