Proceedings of the 2018 International Conference on Digital Health最新文献

{"title":"DrinkWatch: A Mobile Wellbeing Application Based on Interactive and Cooperative Machine Learning","authors":"S. Flutura, A. Seiderer, Ilhan Aslan, C. Dang, Raphael Schwarz, Dominik Schiller, E. André","doi":"10.1145/3194658.3194666","DOIUrl":"https://doi.org/10.1145/3194658.3194666","url":null,"abstract":"We describe in detail the development of DrinkWatch, a wellbeing application, which supports (alcoholic and non-alcoholic) drink activity logging. DrinkWatch runs on a smartwatch device and makes use of machine learning to recognize drink activities based on the smartwatch»s inbuilt sensors. DrinkWatch differs from other mobile machine learning applications by triggering feedback requests from its user in order to cooperatively learn the user»s personalized and contextual drink activities. The cooperative approach aims to reduce limitations in learning performance and to increase the user experience of machine learning based applications. We discuss why the need for cooperative machine learning approaches is increasing and describe lessons that we have learned throughout the development process of DrinkWatch and insights based on initial experiments with users. For example, we demonstrate that six to eight hours of annotated real world data are sufficient to train a reliable base model.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131882774","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":"Machine Learning on Drawing Behavior for Dementia Screening","authors":"K. Tsoi, Max W. Y. Lam, C. Chu, Michael P. F. Wong, H. Meng","doi":"10.1145/3194658.3194659","DOIUrl":"https://doi.org/10.1145/3194658.3194659","url":null,"abstract":"Dementia is a public health problem which is affecting millions of elderly worldwide. Many screening tests are available for early detection on the symptoms of dementia, but most of them are in paper-and-pencil form. The guidance and judgment on test performance are heavily relied on healthcare professionals, but the subjective evaluation always incurs human bias. With advancement of technology, screening tests can be digitalized into computing format, and performed in any portable devices. Geometric drawing is one of the common questions among the screening tools, and digital screening platforms can real-time capture the drawing behavior which directly reflects the brain response during the screening. We had developed a platform to capture the drawing behavior and invited participants with different levels of dementia to be screened with this digital test. Aim: We applied machine learning to study the relationship of drawing behavioral data between participants with or without symptoms of dementia, and hypothesized that brain response time when drawing a simple figure can be digitalized for early detection of dementia. Methods: Patients diagnosed with moderate-to-severe stage of Alzheimer's disease (AD) were recruited from dementia clinics in Hong Kong. People without clinical symptoms of dementia were recruited from local community centers. Montreal Cognitive Assessment (MoCA) test was done in all subjects before screening with the digital screening test. AD patients were classified with MoCA∠22, and healthy subjects were with MoCA'22 as suggested by Tan etal. [1] All participants had to draw two interlocking pentagons using their fingers on the touch screen in a tablet with reference to a sample figure. The drawing processes were modelled by Markov chains of order m, with n states of two continuous variables - drawing velocity and drawing direction. To transit from one state to another, for continuous variable we need a transition function instead of transition matrix. Gaussian processes were employed to specify the set of transition functions as distributions. This maintained a probabilistic tractability for Bayesian inference. Together the resultant combination of models is coined Gaussian process Markov Chains (GPMC). To maximizing specificity and sensitivity, we determined an optimal cut-off by plotting a Receiver Operating Characteristic (ROC) curve. The performance of the drawing platform was compared to the human judgement with reference to the scoring standard in the traditional screening test, the Mini-Mental State Examination (MMSE). Confidence intervals were calculated using Clopper-Pearson exact method. Results: A total of 798 participates was recruited, and 519 (65.0%) of them were classified with AD. The average age of AD patients was 80.3 years (SD=6.5), and average MoCA scores of 14.6 (SD=4.8). The median drawing time of the interlocking pentagons was 17.5 seconds. In the 279 healthy subjects, the average age was 75.5 years (SD","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128282464","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":"Screening Dyslexia for English Using HCI Measures and Machine Learning","authors":"Luz Rello, E. Romero, M. Rauschenberger, Abdullah Ali, Kristin Williams, Jeffrey P. Bigham, N. C. White","doi":"10.1145/3194658.3194675","DOIUrl":"https://doi.org/10.1145/3194658.3194675","url":null,"abstract":"More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that predict dyslexia by observing how people interact with a linguistic computer-based game. We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that people with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes. . Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122371322","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":"Does Journaling Encourage Healthier Choices?: Analyzing Healthy Eating Behaviors of Food Journalers","authors":"Palakorn Achananuparp, Ee-Peng Lim, Vibhanshu Abhishek","doi":"10.1145/3194658.3194663","DOIUrl":"https://doi.org/10.1145/3194658.3194663","url":null,"abstract":"Past research has shown the benefits of food journaling in promoting mindful eating and healthier food choices. However, the links between journaling and healthy eating have not been thoroughly examined. Beyond caloric restriction, do journalers consistently and sufficiently consume healthful diets? How different are their eating habits compared to those of average consumers who tend to be less conscious about health? In this study, we analyze the healthy eating behaviors of active food journalers using data from MyFitnessPal. Surprisingly, our findings show that food journalers do not eat as healthily as they should despite their proclivity to health eating and their food choices resemble those of the general populace. Furthermore, we find that the journaling duration is only a marginal determinant of healthy eating outcomes and sociodemographic factors, such as gender and regions of residence, are much more predictive of healthy food choices.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125530121","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":"Design and Implementation of a Web-based Application to Assess Cognitive Impairment in Affective Disorder","authors":"P. Hafiz, K. Miskowiak, L. Kessing, J. Bardram","doi":"10.1145/3194658.3194691","DOIUrl":"https://doi.org/10.1145/3194658.3194691","url":null,"abstract":"Affective disorder causes mood disturbance and includes depression and bipolar disorder. Cognitive impairment is one of the determinants of poor functioning in patients suffering from an affective disorder. For example, memory impairment in bipolar patients brings about confusion in their daily life. Other cognitive domains include attention, executive function, and psychomotor speed. Cognitive function of these patients are assessed by means of neuropsychological tests such as California Verbal Learning Test (CVLT) and Trail Making Test (TMT) that are used to examine verbal memory and psychomotor speed, respectively.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127790512","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":"Using Mathematical Modeling to Simulate Chagas Disease Spread by Congenital and Blood Transfusion Routes","authors":"Edneide Ramalho, D. Codina, C. Prats, C. Cristino, Virginia Lorena, J. Albuquerque","doi":"10.1145/3194658.3194661","DOIUrl":"https://doi.org/10.1145/3194658.3194661","url":null,"abstract":"Chagas disease is an important health problem in Latin America. Due to the mobility of Latin American population, the disease has spread to other countries. In this work, we used a mathematical model to gain insight into the disease dynamics in a scenario without vector presence as well as to assess the epidemiological effects provided by control strategies.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122419478","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":"An Ontology of Psychological Barriers to Support Behaviour Change","authors":"Yousef Alfaifi, F. Grasso, V. Tamma","doi":"10.1145/3194658.3194680","DOIUrl":"https://doi.org/10.1145/3194658.3194680","url":null,"abstract":"Helping people to adopt and maintain healthier lifestyles is a primary goal of behaviour change interventions. Successful interventions need to account for different barriers (informational, environmental, or psychological) that prevent people from engaging in healthy behaviours. Computational approaches to modelling these interventions focus primarily on informational needs, or on persuasive techniques. The study presented in this paper is specifically aimed at creating a formal conceptual model of the psychological notion of barriers to healthy behaviour, by means of an ontology,i.e. an explicit and machine readable specification of a conceptualisation shared by all the stakeholders~citeStuder-et-al98. The model accounts for other related patient concepts to understand patient behaviour better. This machine-readable knowledge can function as a background to finding the right interventions for behaviour change. Whilst the model is generic and expandable to include other diseases and behaviours, our study uses type 2 diabetes to contextualise the problem of behaviour change.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"88 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126303188","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":"Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning","authors":"F. Gräßer, S. Kallumadi, H. Malberg, S. Zaunseder","doi":"10.1145/3194658.3194677","DOIUrl":"https://doi.org/10.1145/3194658.3194677","url":null,"abstract":"Online review sites and opinion forums contain a wealth of information regarding user preferences and experiences over multiple product domains. This information can be leveraged to obtain valuable insights using data mining approaches such as sentiment analysis. In this work we examine online user reviews within the pharmaceutical field. Online user reviews in this domain contain information related to multiple aspects such as effectiveness of drugs and side effects, which make automatic analysis very interesting but also challenging. However, analyzing sentiments concerning the various aspects of drug reviews can provide valuable insights, help with decision making and improve monitoring public health by revealing collective experience. In this preliminary work we perform multiple tasks over drug reviews with data obtained by crawling online pharmaceutical review sites. We first perform sentiment analysis to predict the sentiments concerning overall satisfaction, side effects and effectiveness of user reviews on specific drugs. To meet the challenge of lacking annotated data we further investigate the transferability of trained classification models among domains, i.e. conditions, and data sources. In this work we show that transfer learning approaches can be used to exploit similarities across domains and is a promising approach for cross-domain sentiment analysis.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134182734","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":"Simulation and Sensitivity Analysis of Sensors Network for Cardiac Monitoring","authors":"Yaël Kolasa, T. Bastogne, J. Georges","doi":"10.1145/3194658.3194686","DOIUrl":"https://doi.org/10.1145/3194658.3194686","url":null,"abstract":"This study's aim was to create a modelisation, and a simulation of a wireless sensor network in conjunction with the use of sensitivity analysis, robust analysis, and multicriteria optimization. The idea behind this is to use this technology in the medical scope of home cardiac monitoring. After an initial phase of research to find the right network simulator, the definition of the simulation parameters has started the robust analysis and sensitivity analysis using HDMR method. Next stage was to implement this method into Matlab, and to define a communication protocol between Matlab and the simulator, so they can exchange parameters and results. At last, gathered data analysis will help to define a product with optimized characteristics.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115955328","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":"Robust Laughter Detection for Wearable Wellbeing Sensing","authors":"Gerhard Johann Hagerer, N. Cummins, F. Eyben, Björn Schuller","doi":"10.1145/3194658.3194693","DOIUrl":"https://doi.org/10.1145/3194658.3194693","url":null,"abstract":"To build a noise-robust online-capable laughter detector for behavioural monitoring on wearables, we incorporate context-sensitive Long Short-Term Memory Deep Neural Networks. We show our solution»s improvements over a laughter detection baseline by integrating intelligent noise-robust voice activity detection (VAD) into the same model. To this end, we add extensive artificially mixed VAD data without any laughter targets to a small laughter training set. The resulting laughter detection enhancements are stable even when frames are dropped, which happen in low resource environments such as wearables. Thus, the outlined model generation potentially improves the detection of vocal cues when the amount of training data is small and robustness and efficiency are required.","PeriodicalId":216658,"journal":{"name":"Proceedings of the 2018 International Conference on Digital Health","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121922815","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}