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"abstract": "Lately, new methods for the acquisition of time-varying, volumetric data for photo-realistic rendering of semi-transparent, volumetric phenomena like fire and smoke have been developed. This paper presents a wavelet-coding and rendering approach for these volumetric sequences that exploits spatial as well as temporal coherence in the data. A space partitioning tree allows for efficient storage and real-time rendering of dynamic, volumetric data on common PC hardware.",
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"abstract": "With the advantages of small size and light weight, electrical stimulation devices have been investigated for providing haptic feedback in relation to virtual objects. Electrical stimulation devices can directly activate sensory receptors to produce reaction force or touch sensations. In the current study, we tested a new method for inducing electrical force sensation in the fingertip, presenting haptic feedback designed to alter softness, hardness and stickiness perception. We developed a 3D virtual reality system combined with finger-motion capture and electrical stimulation devices. The system can provide visual feedback and the sensation of illusory force that moved the index finger by forward-flexion or backward-extension using tendon or cathodic stimulation. In the demo, participants can experience the sensation of softness, hardness and stickiness of a virtual object.",
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"title": "A Perceptual Matching Technique for Depth Judgments in Optical, See-Through Augmented Reality",
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"abstract": "A fundamental problem in optical, see-through augmented reality (AR) is characterizing how it affects the perception of spatial layout and depth. This problem is important because AR system developers need to both place graphics in arbitrary spatial relationships with real-world objects, and to know that users will perceive them in the same relationships. Furthermore, AR makes possible enhanced perceptual techniques that have no real-world equivalent, such as x-ray vision, where AR users are supposed to perceive graphics as being located behind opaque surfaces. This paper reviews and discusses techniques for measuring egocentric depth judgments in both virtual and augmented environments. It then describes a perceptual matching task and experimental design for measuring egocentric AR depth judgments at medium- and far-field distances of 5 to 45 meters. The experiment studied the effect of field of view, the x-ray vision condition, multiple distances, and practice on the task. The paper relates some of the findings to the well-known problem of depth underestimation in virtual environments, and further reports evidence for a switch in bias, from underestimating to overestimating the distance of AR-presented graphics, at 23 meters. It also gives a quantification of how much more difficult the x-ray vision condition makes the task, and then concludes with ideas for improving the experimental methodology.",
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"content": "A fundamental problem in optical, see-through augmented reality (AR) is characterizing how it affects the perception of spatial layout and depth. This problem is important because AR system developers need to both place graphics in arbitrary spatial relationships with real-world objects, and to know that users will perceive them in the same relationships. Furthermore, AR makes possible enhanced perceptual techniques that have no real-world equivalent, such as x-ray vision, where AR users are supposed to perceive graphics as being located behind opaque surfaces. This paper reviews and discusses techniques for measuring egocentric depth judgments in both virtual and augmented environments. It then describes a perceptual matching task and experimental design for measuring egocentric AR depth judgments at medium- and far-field distances of 5 to 45 meters. The experiment studied the effect of field of view, the x-ray vision condition, multiple distances, and practice on the task. The paper relates some of the findings to the well-known problem of depth underestimation in virtual environments, and further reports evidence for a switch in bias, from underestimating to overestimating the distance of AR-presented graphics, at 23 meters. It also gives a quantification of how much more difficult the x-ray vision condition makes the task, and then concludes with ideas for improving the experimental methodology.",
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"affiliation": "Mississippi State University",
"fullName": "J. Edward II Swan",
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"fullName": "Mark A. Livingston",
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"abstract": "Accurately describing and detecting 2D and 3D key-points is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D local feature descriptors and detectors having been proposed, the derivation of a shared descriptor and joint keypoint detector that directly matches pixels and points remains under-explored by the community. This work takes the initiative to establish fine-grained correspondences between 2D images and 3D point clouds. In order to directly match pixels and points, a dual fully-convolutional framework is presented that maps 2D and 3D inputs into a shared latent representation space to simultaneously describe and detect keypoints. Furthermore, an ultra-wide reception mechanism and a novel loss function are designed to mitigate the intrinsic information variations between pixel and point local regions. Extensive experimental results demonstrate that our framework shows competitive performance in fine-grained matching between images and point clouds and achieves state-of-the-art results for the task of indoor visual localization. Our source code is available at https://github.com/BingCS/P2-Net.",
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"abstract": "This paper presents a monocular indirect SLAM system which performs robust initialization and accurate localization. For initialization, we utilize a matrix factorization-based method. Matrix factorization-based methods require that extracted feature points must be tracked in all used frames. Since consistent tracking is difficult in challenging environments, a geometric interpolation that utilizes epipolar geometry is proposed. For localization, 3D lines are utilized. We propose the use of Plücker line coordinates to represent geometric information of lines. We also propose orthonormal representation of Plücker line coordinates and Jacobians of lines for better optimization. Experimental results show that the proposed initialization generates consistent and robust map in linear time with fast convergence even in challenging scenes. And localization using proposed line representations is faster, more accurate and memory efficient than other state-of-the-art methods.",
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"abstract": "Simultaneous localization and mapping (SLAM) algorithms aimed for autonomous vehicles (AVs) are required to utilize sensor redundancies specific to AVs and enable accurate, fast and repeatable estimations of pose and path trajectories. In this work, we present a combination of three SLAM algorithms that utilize a different subset of available sensors such as inertial measurement unit (IMU), a gray-scale mono-camera, and a Lidar. Also, we propose a novel acceleration-based gravity direction initialization (AGI) method for the visual-inertial SLAM algorithm. We analyze the SLAM algorithms and initialization methods for pose estimation accuracy, speed of convergence and repeatability on the KITTI odometry sequences. The proposed VI-SLAM with AGI method achieves relative pose errors less than 2%, convergence in half a minute or less and convergence time variability less than 3s, which makes it preferable for AVs.",
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"affiliation": "Dept. of Electr. & Comput. Eng., Chalmers Univ. of Technol., Goteborg, Sweden",
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"affiliation": "Dept. of Electr. & Comput. Eng., Chalmers Univ. of Technol., Goteborg, Sweden",
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"abstract": "Investigating human mobility patterns and comprehending the social dynamics that govern people movements is of high interest for multiple aspects and reasons. Location-based services, mobile network management, and urban planning are just few of the several applications that benefit fromthis kind of assessment. This work focuses on the stochasticanalysis of spatiotemporal and social network data in order tobuild a human behaviour model which aims to predict socialdynamics and to infer users' mobility patterns and interests.",
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"abstract": "Analysis of people trajectories is key for implementing effective urban computing applications. Nowadays, social media represent one of the main sources of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to predict human mobility by exploiting Twitter data. The prediction method is based on a hybrid approach combining frequent pattern mining, trajectory similarity and supervised classification. The trajectory pattern similarity allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. If none of the patterns satisfies the similarity threshold, a set of spatio-temporal features characterizing locations and movements among them are combined into a supervised learning approach based on M5 model trees. The experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the user's next places achieving a remarkable accuracy and prediction rate.",
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"abstract": "There is an increasing attention of Online Health Communities (OHCs) where health consumers exchange informational and emotional support from their peers. However, the information overload issue makes it difficult for e-patients to identify relevant threads for their needs. An effective thread recommendation approach is highly desirable for OHCs to improve user experience. In this work, we propose to represent OHCs data as a Heterogeneous Healthcare Information Network (HHIN). We extract node-based and path-based features, and train a binary classification model for personalized thread recommendation. We conduct an experiment using a dataset from a popular OHC, and the results show that our approach is promising in predicting users' preference in threads.",
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"abstract": "Our paper focuses on the effect that offline interactions have on the sociability of participants of online communities. We present the findings of a longitudinal study of an invitation-only online community of cigar smokers, tracing the interactions of its participants over a period of eighteen months. We identify the emergence of distinctive patterns of interaction that persist over the course of the study, and explore their effect on the sociability of the participants of the online community. The identified interactions are associated with a mix of behaviors that include play, trading and gifting, and entail the exchange or flow of informational and material objects. Our findings demonstrate that offline interactions play a significant role for the social networking of participants on online communities, and have a positive effect on their online sociability over time. We provide novel insights regarding online participation and offline interactivity, significant for both academics and practitioners.",
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"abstract": "Online Health Communities (OHCs) have become a major source of social support for people with health problems. Using a case study of an OHC among breast cancer survivors, we revealed the types of social support embedded in each post using text-mining techniques. Then we aggregated users' activities related to different types of social support and identified different roles that users play in an OHC via unsupervised machine learning techniques. By analyzing how users' roles change over time, we constructed a transition graph to illustrate the evolution of users' roles in the OHC. We also discovered that a user's reception of social support is correlated with the evolution of her role. In addition, by examining whom a user interacted with, we found evidence for \"role diffusion\"&nashusers tend to adopt roles of her network neighbors. Our research has implications for OHC operators to manage and sustain an OHC.",
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"abstract": "Nowadays, healthcare issues draw people's attention globally. According to the report by the Pew Research Center [1], 35% of U.S. Adults have gone online particularly for information related to medical conditions. Besides getting information from healthcare professionals and friends, 24% of adults also sought information or support from peers who have the same health condition. A major venue where people find such peers is Online Health Communities (OHCs), such as \"Patientslikeme.com\". Compared with traditional health-related websites that only allow users to retrieve information, OHCs increased members' ability to interact with peers facing similar health problems and, as a result, better meet their needs for social support. Literatures on social support suggest that OHCs mainly feature three types of social support: informational support, emotional support, and companionship (a.k.a., Network support) [2]. As social support is a pillar of OHCs, a natural question to ask would be: when it comes to users' participations, are a user's online activities in different types of social support related to her/his participation in an OHC? If so, can we predict whether a user will churn from an OHC based on these social support activities? Despite the large amount of research on social support in OHCs, few studies have answered this question systematically by examining users' seeking, receiving, and provision of various types of social support from large-scale datasets. In 2012, Wang [3] suggested that receiving more emotional support is associated with users' longer stay in an OHC. However, the types of social support investigated were limited and only the receiving of support was considered, while that providing social support is also important and beneficial. Analyzing large-scale data from an OHC, we combined various data analytics techniques, including text mining, survival analysis, and predictive modelling. We found that receiving more emotional support or contributing more companionship are positively correlated with users' longer involvement in OHCs. Our research described as well as predicted users' participation in OHCs from the perspective of online social support. At the same time, we identified several roles of users in an OHC and suggested that overall those who only seek social support are more likely to leave (i.e., \"churn\" from) an OHC. By analyzing their monthly behaviors in social support, we found that users' roles evolve over time. Moreover, we also found evidence for the diffusion of roles in the OHC social network -- a user tends to adopt the role that many of her network neighbors take. This is the first study to show that user roles related to social support are in fact \"contagious\" via social ties. The outcome of this research has implications for building and sustaining an active OHC.",
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"abstract": "In online health-care community (OHC), patients can choose which physician would provide the expert consultation service. However, the success of registered service depends on many factors including online contributions by physicians in the OHC. This study investigates the moderating effect of physicians' endorsement on their online workload. With data from a leading OHC in China, we designed an empirical study involving 140156 records. The results show that for the physician's contribution behavior, the standardized coefficient was 0.317, and the standardized coefficient of the physician's popularity was 1.588. All estimates were statistically significantly at a 0.1% level. Our study discovered that the popularity of physicians plays a main role in gaining registered service, and their online contributions also increase the volume of this service. Moreover, the physicians from tertiary hospitals, with more thank letters and from larger cities were more potential to gain OHC service, comparing with physicians with higher position, with longer profile words, awards and longer relationship time.",
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"content": "In online health-care community (OHC), patients can choose which physician would provide the expert consultation service. However, the success of registered service depends on many factors including online contributions by physicians in the OHC. This study investigates the moderating effect of physicians' endorsement on their online workload. With data from a leading OHC in China, we designed an empirical study involving 140156 records. The results show that for the physician's contribution behavior, the standardized coefficient was 0.317, and the standardized coefficient of the physician's popularity was 1.588. All estimates were statistically significantly at a 0.1% level. Our study discovered that the popularity of physicians plays a main role in gaining registered service, and their online contributions also increase the volume of this service. Moreover, the physicians from tertiary hospitals, with more thank letters and from larger cities were more potential to gain OHC service, comparing with physicians with higher position, with longer profile words, awards and longer relationship time.",
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"abstract": "While online social media offers a way for ignored or stifled voices to be heard, it also allows users a platform to spread hateful speech. Such speech usually originates in fringe communities, yet it can spill over into mainstream channels. In this paper, we measure the impact of joining fringe hateful communities in terms of hate speech propagated to the rest of the social network. We leverage data from Reddit to assess the effect of joining one type of echo chamber: a digital community of like-minded users exhibiting hateful behavior. We measure members' usage of hate speech outside the studied community before and after they become active participants. Using Interrupted Time Series (ITS) analysis as a causal inference method, we gauge the spillover effect, in which hateful language from within a certain community can spread outside that community by using the level of out-of-community hate word usage as a proxy for learned hate. We investigate four different Reddit sub-communities (subreddits) covering three areas of hate speech: racism, misogyny and fat-shaming. In all three cases we find an increase in hate speech outside the originating community, implying that joining such community leads to a spread of hate speech throughout the platform. Moreover, users are found to pick up this new hateful speech for months after initially joining the community. We show that the harmful speech does not remain contained within the community. Our results provide new evidence of the harmful effects of echo chambers and the potential benefit of moderating them to reduce adoption of hateful speech.",
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"abstract": "In recent years, many users join online health communities (OHC) to obtain information and seek social support. One of the most important type of support that a patient needs is to obtain suggestions and information when they make decisions in their diagnosis and treatments. To understand patient decision making processes, we propose to identify the threads on OHC discussion forum that are about patient decision making, and then analyze the questions that patients have. We use deep learning based model to identify such threads. Experiment results show that the proposed methods achieve good performance in precision, recall, F1 score, accuracy, and AUC. Then we leverage topic modeling techniques to analyze the questions that patients expressed in those threads to get a better understanding of patient decision making.",
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"affiliation": "New Jersey Institute of Technology,Department of Computer Science,Newark,USA",
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"abstract": "The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009–2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013–2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013–2014.",
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"content": "The study objective is to develop a big spatial data model to predict the epidemiological impact of influenza in Vellore, India. Large repositories of geospatial and health data provide vital statistics on surveillance and epidemiological metrics, and valuable insight into the spatiotemporal determinants of disease and health. The integration of these big data sources and analytics to assess risk factors and geospatial vulnerability can assist to develop effective prevention and control strategies for influenza epidemics and optimize allocation of limited public health resources. We used the spatial epidemiology data of the HIN1 epidemic collected at the National Informatics Center during 2009–2010 in Vellore. We developed an ecological niche model based on geographically weighted regression for predicting influenza epidemics in Vellore, India during 2013–2014. Data on rainfall, temperature, wind speed, humidity and population are included in the geographically weighted regression analysis. We inferred positive correlations for H1N1 influenza prevalence with rainfall and wind speed, and negative correlations for H1N1 influenza prevalence with temperature and humidity. We evaluated the results of the geographically weighted regression model in predicting the spatial distribution of the influenza epidemic during 2013–2014.",
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"abstract": "Tumor is a frequent abnormality of the human breast that can transform into an incurable condition if left untreated at earlier stage. Thus, early detection of tumors is crucial for successful treatment of tumor anomalies. Due to the distinct thermal characteristics of tumor affected tissues namely heat transfer coefficient and metabolic heat generation; bio heat transfer equations can be a viable option to model the temperature profile of the affected breast in order to pinpoint the faulty tissues. This study develops a two-dimensional model of a tumor-affected breast and investigates the temperature profile of it with varied realistic values of metabolic heat generation of the tumor affected area and the heat transfer coefficient at the affected boundary along with the variation of tumors size. A finite element scheme using Pennes’ bio heat transfer equation is formulated and implemented for solving the model. Firstly, the dimensional effect of tumor is observed by varying tumor radius from 0.25cm to 0.75cm within a breast section of 9cm radius. It is found that the presence of tumor of any size increases the local temperature. Secondly, it is observed that an increase in the metabolic heat generation of the tumor increases the temperature of the respective region. Furthermore, it is found that an increase in the coefficient of heat transfer at the exposed breast surface decreases the temperature at that region. The findings of this study are significant to assess patients’ condition and develop thermography application scheme for the early detection of tumor.",
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"abstract": "Electronic health record (EHR) systems store longitudinal medical data that allows for retrospective studies in healthcare. An ideal EHR would clearly designate when patient data starts and ends, allowing maximal data utility in retrospective studies. However, with differences in EHR implementation, and patients receiving treatment across many healthcare systems, timelines for reliable longitudinal medical data are often unclear, and retrospective studies include suboptimal data. To better identify reliable EHR data for retrospective studies, we built metrics to restrict and weight data based on availability. Our metrics measure the rise and persistence of three different datatypes in an EHR: billing codes, medication events, and tumor registry diagnoses. We implemented our metrics in a generalized cohort creation heuristic to select cohorts with reliable data. We applied our heuristic to select a cohort of stage I-III breast cancer patients at Vanderbilt University Medical Venter (VUMC) for a retrospective study on five-year adjuvant endocrine therapy adherence. Recent clinical trials report five-year adherence at 85%, but studies in the general patient population report lower five-year adherence rates. With our heuristic, we determined a five-year adherence rate bounded between 55% and 78%.",
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"content": "Electronic health record (EHR) systems store longitudinal medical data that allows for retrospective studies in healthcare. An ideal EHR would clearly designate when patient data starts and ends, allowing maximal data utility in retrospective studies. However, with differences in EHR implementation, and patients receiving treatment across many healthcare systems, timelines for reliable longitudinal medical data are often unclear, and retrospective studies include suboptimal data. To better identify reliable EHR data for retrospective studies, we built metrics to restrict and weight data based on availability. Our metrics measure the rise and persistence of three different datatypes in an EHR: billing codes, medication events, and tumor registry diagnoses. We implemented our metrics in a generalized cohort creation heuristic to select cohorts with reliable data. We applied our heuristic to select a cohort of stage I-III breast cancer patients at Vanderbilt University Medical Venter (VUMC) for a retrospective study on five-year adjuvant endocrine therapy adherence. Recent clinical trials report five-year adherence at 85%, but studies in the general patient population report lower five-year adherence rates. With our heuristic, we determined a five-year adherence rate bounded between 55% and 78%.",
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"abstract": "Longitudinal health records contain data on patients' visits, condition, treatment, and test results representing progression of their health status over time. In poorly understood patient populations, such data are particularly helpful in characterizing disease progression and early detection. In this work we developed a deep learning algorithm for temporal pattern discovery over Rochester Epidemiology Project data. We modeled each patient's records as a matrix of temporal clinical events with ICD9 and HCUP CSS diagnosis codes as rows and years of diagnosis as columns. Patients aged 18 or younger at the time of diagnosis were selected. A deep Boltzmann machine network with three hidden layers was constructed with each patient's diagnosis matrix values as visible nodes. The final weights of the network model were analyzed as the common features among patients' records.",
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"content": "Longitudinal health records contain data on patients' visits, condition, treatment, and test results representing progression of their health status over time. In poorly understood patient populations, such data are particularly helpful in characterizing disease progression and early detection. In this work we developed a deep learning algorithm for temporal pattern discovery over Rochester Epidemiology Project data. We modeled each patient's records as a matrix of temporal clinical events with ICD9 and HCUP CSS diagnosis codes as rows and years of diagnosis as columns. Patients aged 18 or younger at the time of diagnosis were selected. A deep Boltzmann machine network with three hidden layers was constructed with each patient's diagnosis matrix values as visible nodes. The final weights of the network model were analyzed as the common features among patients' records.",
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"abstract": "There have been a number of studies concerning inductive rule generation from databases. All inductive rules are based on instances of the databases, and such instances can be regarded as the sample of the population in the real world. Therefore, the validity, unbiasedness and correctness of these instances cannot be overemphasized in the rule induction environment. While many researchers have focused on the methods of generating rules from databases, the author discusses some sampling issues that occur in rule generation from databases. The author tries to bridge the gap between sampling in statistics and rule generation in databases. Two sampling problems-small sample size and biased sample-which occur mostly in rule induction were studied. The author investigates how these problems affect the validity of rule induction and provides a set of criteria for a rule induction system to generate feasible rules.",
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"content": "There have been a number of studies concerning inductive rule generation from databases. All inductive rules are based on instances of the databases, and such instances can be regarded as the sample of the population in the real world. Therefore, the validity, unbiasedness and correctness of these instances cannot be overemphasized in the rule induction environment. While many researchers have focused on the methods of generating rules from databases, the author discusses some sampling issues that occur in rule generation from databases. The author tries to bridge the gap between sampling in statistics and rule generation in databases. Two sampling problems-small sample size and biased sample-which occur mostly in rule induction were studied. The author investigates how these problems affect the validity of rule induction and provides a set of criteria for a rule induction system to generate feasible rules.",
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"abstract": "This paper reports our effort to establish the desirable characteristics for the next generation asthma APP for an underserved population. Proposed asthma mobile APP aims to promote older adults' positive adjustment to this chronic disease by being an effective tool for patients to track their personal asthma triggers, predict asthma attacks, support asthma self-management and communicate with healthcare provider. Management of asthma is a dynamic process and varies by individual. For that reason, a personalized asthma APP is necessary to control this chronic disease. Environmental indicators, personal triggers, symptoms monitoring, medication use, peak flow, and blood oxygen monitoring data are analyzed to predict an asthma attack or indicate control. Other non-asthma symptom monitoring, such as fatigue, and biometric measures, like blood pressure, may be added as requested by end user.",
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"content": "This paper reports our effort to establish the desirable characteristics for the next generation asthma APP for an underserved population. Proposed asthma mobile APP aims to promote older adults' positive adjustment to this chronic disease by being an effective tool for patients to track their personal asthma triggers, predict asthma attacks, support asthma self-management and communicate with healthcare provider. Management of asthma is a dynamic process and varies by individual. For that reason, a personalized asthma APP is necessary to control this chronic disease. Environmental indicators, personal triggers, symptoms monitoring, medication use, peak flow, and blood oxygen monitoring data are analyzed to predict an asthma attack or indicate control. Other non-asthma symptom monitoring, such as fatigue, and biometric measures, like blood pressure, may be added as requested by end user.",
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"title": "Self-Service Data Preprocessing and Cohort Analysis for Medical Researchers",
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"abstract": "Medical researchers are increasingly interested in data-driven approaches to support informed decisions in many medical areas. They collect data about the patients they treat, often creating their own specialized data tables with more characteristics than what is defined in their clinical information system (CIS). Usually, these data tables or sEHR (small electronical health records) are rather small, maybe containing the data of only hundreds of patients. Medical researchers are struggling to find an easy way to first clean and transform these sEHR, and then create cohorts and perform confirmative or exploratory analysis. This paper introduces a methodology and identifies requirements for building systems for self-service data preprocessing and cohort analysis for medical researchers. We also describe a system based on this methodology and the requirements that shows the benefits of our approach. We further highlight these benefits with an example scenario from our projects with clinicians specialized on head&neck cancer treatment.",
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"fno": "08945040",
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