Query: Innovative information visualization of electronic health record data: a systematic review

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A visual analytics agenda <=SEP=> Researchers have made significant progress in disciplines such as scientific and information visualization, statistically based exploratory and confirmatory analysis, data and knowledge representations, and perceptual and cognitive sciences. Although some research is being done in this area, the pace at which new technologies and technical talents are becoming available is far too slow to meet the urgent need. National Visualization and Analytics Center's goal is to advance the state of the science to enable analysts to detect the expected and discover the unexpected from massive and dynamic information streams and databases consisting of data of multiple types and from multiple sources, even though the data are often conflicting and incomplete. Visual analytics is a multidisciplinary field that includes the following focus areas: (i) analytical reasoning techniques, (ii) visual representations and interaction techniques, (iii) data representations and transformations, (iv) techniques to support production, presentation, and dissemination of analytical results. The R&D agenda for visual analytics addresses technical needs for each of these focus areas, as well as recommendations for speeding the movement of promising technologies into practice. This article provides only the concise summary of the R&D agenda. We encourage reading, discussion, and debate as well as active innovation toward the agenda for visual analysis.

An Introduction to Variable and Feature Selection <=SEP=> Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variabl es are available. These areas include text processing of internet documents, gene expression arr ay nalysis, and combinatorial chemistry. The objective of variable selection is three-fold: improvi ng the prediction performance of the predictors, providing faster and more cost-effective predict ors, and providing a better understanding of the underlying process that generated the data. The contrib utions of this special issue cover a wide range of aspects of such problems: providing a better definit ion of the objective function, feature construction, feature ranking, multivariate feature sele ction, efficient search methods, and feature validity assessment methods.

Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard Medical Practice Study I. <=SEP=> BACKGROUND
As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care.


METHODS
We reviewed 30,121 randomly selected records from 51 randomly selected acute care, nonpsychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians.


RESULTS
Adverse events occurred in 3.7 percent of the hospitalizations (95 percent confidence interval, 3.2 to 4.2), and 27.6 percent of the adverse events were due to negligence (95 percent confidence interval, 22.5 to 32.6). Although 70.5 percent of the adverse events gave rise to disability lasting less than six months, 2.6 percent caused permanently disabling injuries and 13.6 percent led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi 2 = 21.04, P less than 0.0001). Using weighted totals, we estimated that among the 2,671,863 patients discharged from New York hospitals in 1984 there were 98,609 adverse events and 27,179 adverse events involving negligence. Rates of adverse events rose with age (P less than 0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (P less than 0.01). There were significant differences in rates of adverse events among categories of clinical specialties (P less than 0.0001), but no differences in the percentage due to negligence.


CONCLUSIONS
There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.

Software developers' perceptions of productivity <=SEP=> The better the software development community becomes at creating software, the more software the world seems to demand. Although there is a large body of research about measuring and investigating productivity from an organizational point of view, there is a paucity of research about how software developers, those at the front-line of software construction, think about, assess and try to improve their productivity. To investigate software developers' perceptions of software development productivity, we conducted two studies: a survey with 379 professional software developers to help elicit themes and an observational study with 11 professional software developers to investigate emergent themes in more detail. In both studies, we found that developers perceive their days as productive when they complete many or big tasks without significant interruptions or context switches. Yet, the observational data we collected shows our participants performed significant task and activity switching while still feeling productive. We analyze such apparent contradictions in our findings and use the analysis to propose ways to better support software developers in a retrospection and improvement of their productivity through the development of new tools and the sharing of best practices.

Real-time techniques for 3D flow visualization <=SEP=> Visualization of three-dimensional steady flow has to overcome a lot of problems to be effective. Among them are occlusion of distant details, lack of directional and depth hints and occlusion. We present methods which address these problems for real-time graphic representations applicable in virtual environments. We use dashtubes, i.e., animated, opacity-mapped streamlines, as a visualization icon for 3D-flow visualization. We present a texture mapping technique to keep the level of texture detail along a streamline nearly constant even when the velocity of the flow varies considerably. An algorithm is described which distributes the dashtubes evenly in space. We apply magic lenses and magic boxes as interaction techniques for investigating densely filled areas without overwhelming the observer with visual detail. Implementation details of these methods and their integration in our virtual environment conclude the paper.

Developing a car gesture interface for use as a secondary task <=SEP=> Existing gesture-interface research has centered on controlling the user's primary task. This paper explores the use of gestures to control secondary tasks while the user is focused on driving. Through contextual inquiry, ten iterative prototypes, and a Wizard of Oz experiment, we show that a gesture interface is a viable alternative for completing secondary tasks in the car.

On multiple foreground cosegmentation <=SEP=> In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general Webuser photo sets where a finite number of K foregrounds of interest repeatedly occur cross the entire photo set, but only an unknown subset of them is presented in each image. This contrasts the classical cosegmentation problem dealt with by most existing algorithms, which assume a much simpler but less realistic setting where the same set of foregrounds recurs in every image. We propose a novel optimization method for MFC, which makes no assumption on foreground configurations and does not suffer from the aforementioned limitation, while still leverages all the benefits of having co-occurring or (partially) recurring contents across images. Our method builds on an iterative scheme that alternates between a foreground modeling module and a region assignment module, both highly efficient and scalable. In particular, our approach is flexible enough to integrate any advanced region classifiers for foreground modeling, and our region assignment employs a combinatorial auction framework that enjoys several intuitively good properties such as optimality guarantee and linear complexity. We show the superior performance of our method in both segmentation quality and scalability in comparison with other state-of-the-art techniques on a newly introduced FlickrMFC dataset and the standard ImageNet dataset.

Modularized Morphing of Neural Networks <=SEP=> In this work we study the problem of network morphism, an effective learning scheme to morph a well-trained neural network to a new one with the network function completely preserved. Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i.e., how a convolutional layer can be morphed into an arbitrary module of a neural network. To simplify the representation of a network, we abstract a module as a graph with blobs as vertices and convolutional layers as edges, based on which the morphing process is able to be formulated as a graph transformation problem. Two atomic morphing operations are introduced to compose the graphs, based on which modules are classified into two families, i.e., simple morphable modules and complex modules. We present practical morphing solutions for both of these two families, and prove that any reasonable module can be morphed from a single convolutional layer. Extensive experiments have been conducted based on the state-of-the-art ResNet on benchmark datasets, and the effectiveness of the proposed solution has been verified.

Face Detection using Digital Image Processing <=SEP=> This paper presents a technique for automatically detecting human faces in digital color images. This is two-step process which first detects regions contain human skin in the color image and then extracts information from these regions which might indicate the location of a face in the image. The skin detection is performed using a skin filter which relies on color and texture information. The face detection is performed on a grayscale image containing only the detected skin areas. A combination of thresh holding and mathematical morphology are used to extract object features that would indicate the presence of a face. The face detection process works predictably and fairly reliably, as test results show.

Regularized linear and kernel redundancy analysis <=SEP=> Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures redundant information in the criterion variables in a most parsimonious way. A ridge type of regularization was introduced in RA to deal with the multicollinearity problem among the predictor variables. The regularized linear RA was extended to nonlinear RA using a kernel method to enhance the predictability. The usefulness of the proposed procedures was demonstrated by a Monte Carlo study and through the analysis of two real data sets.

A design process for embedding knowledge management in everyday work <=SEP=> Knowledge Management Software must be embedded in processes of knowledge workers' everyday practice. In order to attain a seamless design, regarding the special qualities and requirements of knowledge work, detailed studies of the existing work processes and analysis of the used knowledge are necessary. Participation of the knowledge owners and future users us an important factor for success of knowledge management systems. In this paper we describe characteristics of knowledge work motivating the usage of participatory design techniques. We suggest a design process for developing or improving knowledge management, which includes ethnographic surveys, user participation in cyclic improvement, scenario based design, and the use of multiple design artifacts and documents. Finally we explain the benefits of our approach. The paper is based on a case study we carried out to design and introduce a knowledge management system in a training company.

The Development Process of the Semantic Web and Web Ontology <=SEP=> This paper deals with the semantic web and web ontology. The existing ontology development processes are not catered towards casual web ontology development, a notion analogous to standard web page development. Ontologies have become common on the World-Wide Web[2]. Key features of this process include easy and rapid creation of ontological skeletons, searching and linking to existing ontologies and a natural language-based technique to improve presentation of ontologies[6]. Ontologies, however, vary greatly in size, scope and semantics. They can range from generic upper-level ontologies to domain-specific schemas. The success of the Semantic Web is based on the existance of numerous distributed ontologies, using which users can annotate their data, thereby enabling shared machine readable content. This paper elaborates the stages in a casual ontology development process. Key wordsToolkits; ontology; semantic web; language-based; web ontology.

Building upon RouteFlow : a SDN development experience <=SEP=> RouteFlow is a platform for providing virtual IP routing services in OpenFlow networks. During the first year of development, we came across some use cases that might be interesting pursuing in addition to a number of lessons learned worth sharing. In this paper, we will discuss identified requirements and architectural and implementation changes made to shape RouteFlow into a more robust solution for Software Defined networking (SDN). This paper addresses topics of interest to the SDN community, such as development issues involving layered applications on top of network controllers, ease of configuration, and network visualization. In addition, we will present the first publicly known use case with multiple, heterogeneous OpenFlow controllers to implement a centralized routing control function, demonstrating how IP routing as a service can be provided for different network domains under a single central control. Finally, performance comparisons and a real testbed were used as means of validating the implementation.

PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring <=SEP=> Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users' conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.

Wideband planar monopole antennas with dual band-notched characteristics <=SEP=> Wideband planar monopole antennas with dual band-notched characteristics are presented. The proposed antenna consists of a wideband planar monopole antenna and the multiple cup-, cap-, and inverted L-shaped slots, producing band-notched characteristics. In order to generate dual band-notched characteristic, we propose nine types of planar monopole antennas, which have two or three cap (cup or inverted L)-shaped slots in the radiator. This technique is suitable for creating ultra-wideband antenna with narrow frequency notches or for creating multiband antennas

Optimal Operation of Distribution Feeders in Smart Grids <=SEP=> This paper presents a generic and comprehensive distribution optimal power flow (DOPF) model that can be used by local distribution companies (LDCs) to integrate their distribution system feeders into a Smart Grid. The proposed three-phase DOPF framework incorporates detailed modeling of distribution system components and considers various operating objectives. Phase specific and voltage dependent modeling of customer loads in the three-phase DOPF model allows LDC operators to determine realistic operating strategies that can improve the overall feeder efficiency. The proposed distribution system operation objective is based on the minimization of the energy drawn from the substation while seeking to minimize the number of switching operations of load tap changers and capacitors. A novel method for solving the three-phase DOPF model by transforming the mixed-integer nonlinear programming problem to a nonlinear programming problem is proposed which reduces the computational burden and facilitates its practical implementation and application. Two practical case studies, including a real distribution feeder test case, are presented to demonstrate the features of the proposed methodology. The results illustrate the benefits of the proposed DOPF in terms of reducing energy losses while limiting the number of switching operations.

Human error analysis of commercial aviation accidents: application of the Human Factors Analysis and Classification system (HFACS). <=SEP=> BACKGROUND
The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based on Reason's (1990) model of latent and active failures, HFACS addresses human error at all levels of the system, including the condition of aircrew and organizational factors. The purpose of the present study was to assess the utility of the HFACS framework as an error analysis and classification tool outside the military.


METHODS
The HFACS framework was used to analyze human error data associated with aircrew-related commercial aviation accidents that occurred between January 1990 and December 1996 using database records maintained by the NTSB and the FAA.


RESULTS
Investigators were able to reliably accommodate all the human causal factors associated with the commercial aviation accidents examined in this study using the HFACS system. In addition, the classification of data using HFACS highlighted several critical safety issues in need of intervention research.


CONCLUSION
These results demonstrate that the HFACS framework can be a viable tool for use within the civil aviation arena. However, additional research is needed to examine its applicability to areas outside the flight deck, such as aircraft maintenance and air traffic control domains.

A 0.5-to-2.5 Gb/s Reference-Less Half-Rate Digital CDR With Unlimited Frequency Acquisition Range and Improved Input Duty-Cycle Error Tolerance <=SEP=> A reference-less highly digital half-rate clock and data recovery (CDR) circuit with improved tolerance to input duty cycle error is presented. Using a chain of frequency dividers, the pro posed frequency detector produces a known sub-harmonic tone from the incoming random data. A digital frequency-locked loop uses the extracted tone, and drives the oscillator to any sub-rate of the input data frequency. The early/late outputs of a conventional half-rate bang-bang phase detector are used to determine the duty-cycle error in the incoming random data and adjust the oscillator clock phases to maximize receiver timing margins. Fabricated in 0.13 μm CMOS technology, the prototype digital CDR op erates without any errors from 0.5 Gb/s to 2.5 Gb/s. At 2 Gb/s, the prototype consumes 6.1 mW power from a 1.2 V supply. The pro posed clock-phase calibration is capable of correcting upto ±20% of input data duty-cycle error.

Applying data mining techniques in job recommender system for considering candidate job preferences <=SEP=> Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems exist that uses different strategies , here efforts have been put to make the job recommendations on the basis of candidate's profile matching as well as preserving candidate's job behavior or preferences. Firstly, rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidate's own applied jobs history. Through this technique a significant level of accuracy has been achieved over other basic methods of job recommendations.

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks <=SEP=> This paper presents a forecasting model designed using WSNs( Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.

Test Case Prioritization Using Requirements-Based Clustering <=SEP=> The importance of using requirements information in the testing phase has been well recognized by the requirements engineering community, but to date, a vast majority of regression testing techniques have primarily relied on software code information. Incorporating requirements information into the current testing practice could help software engineers identify the source of defects more easily, validate the product against requirements, and maintain software products in a holistic way. In this paper, we investigate whether the requirements-based clustering approach that incorporates traditional code analysis information can improve the effectiveness of test case prioritization techniques. To investigate the effectiveness of our approach, we performed an empirical study using two Java programs with multiple versions and requirements documents. Our results indicate that the use of requirements information during the test case prioritization process can be beneficial.

Teaching and working with robots as a collaboration <=SEP=> New applications for autonomous robots bring them into the human environment where they are to serve as helpful assistants to untrained users in the home or office, or work as capable members of human-robot teams for security, military, and space efforts. These applications require robots to be able to quickly learn how to perform new tasks from natural human instruction, and to perform tasks collaboratively with human teammates. Using joint intention theory as our theoretical framework, our approach integrates learning and collaboration through a goal based task structure. Specifically, we use collaborative discourse with accompanying gestures and social cues to teach a humanoid robot a structurally complex task. Having learned the representation for the task, the robot then performs it shoulder-to-shoulder with a human partner, using social communication acts to dynamically mesh its plans with those of its partner, according to the relative capabilities of the human and the robot.

Sensitive Lifelogs: A Privacy Analysis of Photos from Wearable Cameras <=SEP=> While media reports about wearable cameras have focused on the privacy concerns of bystanders, the perspectives of the `lifeloggers' themselves have not been adequately studied. We report on additional analysis of our previous in-situ lifelogging study in which 36 participants wore a camera for a week and then reviewed the images to specify privacy and sharing preferences. In this Note, we analyze the photos themselves, seeking to understand what makes a photo private, what participants said about their images, and what we can learn about privacy in this new and very different context where photos are captured automatically by one's wearable camera. We find that these devices record many moments that may not be captured by traditional (deliberate) photography, with camera owners concerned about impression management and protecting private information of both themselves and bystanders.

Foundations of Logic Programming <=SEP=> 

Evaluating faces on trustworthiness: an extension of systems for recognition of emotions signaling approach/avoidance behaviors. <=SEP=> People routinely make various trait judgments from facial appearance, and such judgments affect important social outcomes. These judgments are highly correlated with each other, reflecting the fact that valence evaluation permeates trait judgments from faces. Trustworthiness judgments best approximate this evaluation, consistent with evidence about the involvement of the amygdala in the implicit evaluation of face trustworthiness. Based on computer modeling and behavioral experiments, I argue that face evaluation is an extension of functionally adaptive systems for understanding the communicative meaning of emotional expressions. Specifically, in the absence of diagnostic emotional cues, trustworthiness judgments are an attempt to infer behavioral intentions signaling approach/avoidance behaviors. Correspondingly, these judgments are derived from facial features that resemble emotional expressions signaling such behaviors: happiness and anger for the positive and negative ends of the trustworthiness continuum, respectively. The emotion overgeneralization hypothesis can explain highly efficient but not necessarily accurate trait judgments from faces, a pattern that appears puzzling from an evolutionary point of view and also generates novel predictions about brain responses to faces. Specifically, this hypothesis predicts a nonlinear response in the amygdala to face trustworthiness, confirmed in functional magnetic resonance imaging (fMRI) studies, and dissociations between processing of facial identity and face evaluation, confirmed in studies with developmental prosopagnosics. I conclude with some methodological implications for the study of face evaluation, focusing on the advantages of formally modeling representation of faces on social dimensions.

Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures <=SEP=> This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the first approach of its kind. It combines a Variational Autoencoder(VAE) with a Recurrent Attention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. The recurrent attention mechanism in Sync-DRAW attends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is efficient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets.

How schema and novelty augment memory formation <=SEP=> Information that is congruent with existing knowledge (a schema) is usually better remembered than less congruent information. Only recently, however, has the role of schemas in memory been studied from a systems neuroscience perspective. Moreover, incongruent (novel) information is also sometimes better remembered. Here, we review lesion and neuroimaging findings in animals and humans that relate to this apparent paradoxical relationship between schema and novelty. In addition, we sketch a framework relating key brain regions in medial temporal lobe (MTL) and medial prefrontal cortex (mPFC) during encoding, consolidation and retrieval of information as a function of its congruency with existing information represented in neocortex. An important aspect of this framework is the efficiency of learning enabled by congruency-dependent MTL-mPFC interactions.

Neural scene representation and rendering <=SEP=> Scene representation—the process of converting visual sensory data into concise descriptions—is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.