Synergizing human-machine intelligence: Visualizing, labeling, and mining the electronic health record

We live in a world where data surround us in every aspect of our lives. The key challenge for humans and machines is how we can make better use of such data. Imagine what would happen if you were to have intelligent machines that could give you insight into the data. Insight that will enable you to better 1) reason about, 2) learn, and 3) understand the underlying phenomena that produced the data. The possibilities of combined human-machine intelligence are endless and will impact our lives in ways we can not even imagine today. Synergistic human-machine intelligence aims to facilitate the analytical reasoning and inference process of humans by creating machines that maximize a human's ability to 1) reason about, 2) learn, and 3) understand large, complex, and heterogeneous data. Combined human-machine intelligence is a powerful symbiosis of mutual benefit, in which we depend on the computational capabilities of the machine for the tasks we are not good at, and the machine requires human intervention for the tasks it performs poorly on. This relationship provides a compelling alternative to either approach in isolation for solving today's and tomorrow's arising data challenges. In his regard, this dissertation proposes a diverse analytical framework that leverages synergistic human-machine intelligence to maximize a human's ability to better 1) reason about, 2) learn, and 3) understand different biomedical imaging and healthcare data present in the patient's electronic health record (EHR). Correspondingly, we approach the data analyses problem from the 1) visualization, 2) labeling, and 3) mining perspective and demonstrate the efficacy of our analytics on specific application scenarios and various data domains. In the first part of this dissertation we explore the question how we can build intelligent imaging analytics that are commensurate with human capabilities and constraints, specifically for optimizing data visualization and automated labeling workflows. Our journey starts with heuristic rule-based analytical models that are derived from task-specific human knowledge. From this experience, we move on to data-driven analytics, where we adapt and combine the intelligence of the model based on prior information provided by the human and synthetic knowledge learned from partial data observations. Within this realm, we propose a novel Bayesian transductive Markov random field model that requires minimal human intervention and is able to cope with scarce label information to learn and infer object shapes in complex spatial, multimodal, spatio-temporal, and longitudinal data. We then study the question how machines can learn discriminative object representations from dense human provided label information by investigating learning and inference mechanisms that make use of deep learning architectures. The developed analytics can aid visualization and labeling tasks, which enables the interpretation and quantification of clinically relevant image information. The second part explores the question how we can build data-driven analytics for exploratory analysis in longitudinal event data that are commensurate with human capabilities and constraints. We propose human-intuitive analytics that enable the representation and discovery of interpretable event patterns to ease knowledge absorption and comprehension of the employed analytics model and the underlying data. We propose a novel doubly-constrained convolutional sparse-coding framework that learns interpretable and shift-invariant latent temporal event patterns. We apply the model to mine complex event data in EHRs. By mapping the event space to heterogeneous patient encounters in the EHR we explore the linkage between healthcare resource utilization (HRU) in relation to disease severity. This linkage may help to better understand how disease specific co-morbidities and their clinical attributes incur different HRU patterns. Such insight helps to characterize the patient's care history, which then enables the comparison against clinical practice guidelines, the discovery of prevailing practices based on common HRU group patterns, and the identification of outliers that might indicate poor patient management

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

Local selection of features and its applications to image search and annotation

In multimedia applications, direct representations of data objects typically involve hundreds or thousands of features. Given a query object, the similarity between the query object and a database object can be computed as the distance between their feature vectors. The neighborhood of the query object consists of those database objects that are close to the query object. The semantic quality of the neighborhood, which can be measured as the proportion of neighboring objects that share the same class label as the query object, is crucial for many applications, such as content-based image retrieval and automated image annotation. However, due to the existence of noisy or irrelevant features, errors introduced into similarity measurements are detrimental to the neighborhood quality of data objects. One way to alleviate the negative impact of noisy features is to use feature selection techniques in data preprocessing. From the original vector space, feature selection techniques select a subset of features, which can be used subsequently in supervised or unsupervised learning algorithms for better performance. However, their performance on improving the quality of data neighborhoods is rarely evaluated in the literature. In addition, most traditional feature selection techniques are global, in the sense that they compute a single set of features across the entire database. As a consequence, the possibility that the feature importance may vary across different data objects or classes of objects is neglected. To compute a better neighborhood structure for objects in high-dimensional feature spaces, this dissertation proposes several techniques for selecting features that are important to the local neighborhood of individual objects. These techniques are then applied to image applications such as content-based image retrieval and image label propagation. Firstly, an iterative K-NN graph construction method for image databases is proposed. A local variant of the Laplacian Score is designed for the selection of features for individual images. Noisy features are detected and sparsified iteratively from the original standardized feature vectors. This technique is incorporated into an approximate K-NN graph construction method so as to improve the semantic quality of the graph. Secondly, in a content-based image retrieval system, a generalized version of the Laplacian Score is used to compute different feature subspaces for images in the database. For online search, a query image is ranked in the feature spaces of database images. Those database images for which the query image is ranked highly are selected as the query results. Finally, a supervised method for the local selection of image features is proposed, for refining the similarity graph used in an image label propagation framework. By using only the selected features to compute the edges leading from labeled image nodes to unlabeled image nodes, better annotation accuracy can be achieved. Experimental results on several datasets are provided in this dissertation, to demonstrate the effectiveness of the proposed techniques for the local selection of features, and for the image applications under consideration

Text miner's little helper: scalable self-tuning methodologies for knowledge exploration

L'abstract è presente nell'allegato / the abstract is in the attachmen

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Improved integration of information to reduce subsurface model bias

Subsurface modeling deals with data-related issues like cognitive and sampling biases, and model-related challenges including statistical assumptions, misspecification, and algorithmic biases. These challenges introduce four critical implications during subsurface modeling. Firstly, subsurface sampling is subject to sampling bias, which compromises statistical representativeness. Secondly, analog selection methodologies rely on multivariate statistics and expert judgment that overlook spatial information and data dimensionality. Thirdly, subsurface inferential workflows that utilize dimensionality reduction seldom provide repeatable frameworks that maintain model stability and are invariant to Euclidean transformations. Lastly, deep learning methods for dimensionality reduction, characterized as black-box models, lack interpretability and robust evaluation metrics, increasing susceptibility to algorithmic bias. Consequently, neglecting these challenges in subsurface modeling could lead to erroneous predictions, inconsistent inferences, diminished model reliability, and suboptimal decision-making that impacts project economics. This dissertation integrates information within subsurface models to reduce model bias and significantly improve their accuracy, robustness, and generalizability. First, I create spatial declustering methods to debias spatial datasets with single and multiscale preferential sampling in stationary populations. Second, I introduce a novel geostatistics-based machine learning method for identifying subsurface resource analogs that integrate spatial information in subsurface datasets with high dimensionality. Next, I efficiently combine machine learning and computational geometry methods to stabilize lower dimensional spaces for uncertainty quantification and interpretation. Finally, I create a methodology to assess, evaluate, and interpret the stability of deep learning latent feature spaces. These novel methodologies demonstrate the importance of improved techniques for information integration in subsurface modeling and show better results over naïve methods. This results in objective sampling debiasing in spatial stationary populations with single or multiple data scales, improving statistical representativity. Also, the results show better generalization and accurate identification of spatial analogs in high-dimensional datasets. Moreover, the methods yield Euclidean transformation-invariant lower-dimensional spaces, ensuring unique and repeatable solutions that improve model reliability and interpretability, for rational comparisons. Finally, the results indicate that deep learning models for dimensionality reduction exhibit algorithmic biases and instabilities, including sample, structural, and inferential instability, affecting their reliability and interpretability. Together, these innovations ultimately reduce model bias and significantly improve subsurface modeling.Petroleum and Geosystems Engineerin

Persönliche Wege der Interaktion mit multimedialen Inhalten

Today the world of multimedia is almost completely device- and content-centered. It focuses it’s energy nearly exclusively on technical issues such as computing power, network specifics or content and device characteristics and capabilities. In most multimedia systems, the presentation of multimedia content and the basic controls for playback are main issues. Because of this, a very passive user experience, comparable to that of traditional TV, is most often provided. In the face of recent developments and changes in the realm of multimedia and mass media, this ”traditional” focus seems outdated. The increasing use of multimedia content on mobile devices, along with the continuous growth in the amount and variety of content available, make necessary an urgent re-orientation of this domain. In order to highlight the depth of the increasingly difficult situation faced by users of such systems, it is only logical that these individuals be brought to the center of attention. In this thesis we consider these trends and developments by applying concepts and mechanisms to multimedia systems that were first introduced in the domain of usercentrism. Central to the concept of user-centrism is that devices should provide users with an easy way to access services and applications. Thus, the current challenge is to combine mobility, additional services and easy access in a single and user-centric approach. This thesis presents a framework for introducing and supporting several of the key concepts of user-centrism in multimedia systems. Additionally, a new definition of a user-centric multimedia framework has been developed and implemented. To satisfy the user’s need for mobility and flexibility, our framework makes possible seamless media and service consumption. The main aim of session mobility is to help people cope with the increasing number of different devices in use. Using a mobile agent system, multimedia sessions can be transferred between different devices in a context-sensitive way. The use of the international standard MPEG-21 guarantees extensibility and the integration of content adaptation mechanisms. Furthermore, a concept is presented that will allow for individualized and personalized selection and face the need for finding appropriate content. All of which can be done, using this approach, in an easy and intuitive way. Especially in the realm of television, the demand that such systems cater to the need of the audience is constantly growing. Our approach combines content-filtering methods, state-of-the-art classification techniques and mechanisms well known from the area of information retrieval and text mining. These are all utilized for the generation of recommendations in a promising new way. Additionally, concepts from the area of collaborative tagging systems are also used. An extensive experimental evaluation resulted in several interesting findings and proves the applicability of our approach. In contrast to the ”lean-back” experience of traditional media consumption, interactive media services offer a solution to make possible the active participation of the audience. Thus, we present a concept which enables the use of interactive media services on mobile devices in a personalized way. Finally, a use case for enriching TV with additional content and services demonstrates the feasibility of this concept.Die heutige Welt der Medien und der multimedialen Inhalte ist nahezu ausschließlich inhalts- und geräteorientiert. Im Fokus verschiedener Systeme und Entwicklungen stehen oft primär die Art und Weise der Inhaltspräsentation und technische Spezifika, die meist geräteabhängig sind. Die zunehmende Menge und Vielfalt an multimedialen Inhalten und der verstärkte Einsatz von mobilen Geräten machen ein Umdenken bei der Konzeption von Multimedia Systemen und Frameworks dringend notwendig. Statt an eher starren und passiven Konzepten, wie sie aus dem TV Umfeld bekannt sind, festzuhalten, sollte der Nutzer in den Fokus der multimedialen Konzepte rücken. Um dem Nutzer im Umgang mit dieser immer komplexeren und schwierigen Situation zu helfen, ist ein Umdenken im grundlegenden Paradigma des Medienkonsums notwendig. Durch eine Fokussierung auf den Nutzer kann der beschriebenen Situation entgegengewirkt werden. In der folgenden Arbeit wird auf Konzepte aus dem Bereich Nutzerzentrierung zurückgegriffen, um diese auf den Medienbereich zu übertragen und sie im Sinne einer stärker nutzerspezifischen und nutzerorientierten Ausrichtung einzusetzen. Im Fokus steht hierbei der TV-Bereich, wobei die meisten Konzepte auch auf die allgemeine Mediennutzung übertragbar sind. Im Folgenden wird ein Framework für die Unterstützung der wichtigsten Konzepte der Nutzerzentrierung im Multimedia Bereich vorgestellt. Um dem Trend zur mobilen Mediennutzung Sorge zu tragen, ermöglicht das vorgestellte Framework die Nutzung von multimedialen Diensten und Inhalten auf und über die Grenzen verschiedener Geräte und Netzwerke hinweg (Session mobility). Durch die Nutzung einer mobilen Agentenplattform in Kombination mit dem MPEG-21 Standard konnte ein neuer und flexibel erweiterbarer Ansatz zur Mobilität von Benutzungssitzungen realisiert werden. Im Zusammenhang mit der stetig wachsenden Menge an Inhalten und Diensten stellt diese Arbeit ein Konzept zur einfachen und individualisierten Selektion und dem Auffinden von interessanten Inhalten und Diensten in einer kontextspezifischen Weise vor. Hierbei werden Konzepte und Methoden des inhaltsbasierten Filterns, aktuelle Klassifikationsmechanismen und Methoden aus dem Bereich des ”Textminings” in neuer Art und Weise in einem Multimedia Empfehlungssystem eingesetzt. Zusätzlich sind Methoden des Web 2.0 in eine als Tag-basierte kollaborative Komponente integriert. In einer umfassenden Evaluation wurde sowohl die Umsetzbarkeit als auch der Mehrwert dieser Komponente demonstriert. Eine aktivere Beteiligung im Medienkonsum ermöglicht unsere iTV Komponente. Sie unterstützt das Anbieten und die Nutzung von interaktiven Diensten, begleitend zum Medienkonsum, auf mobilen Geräten. Basierend auf einem Szenario zur Anreicherung von TV Sendungen um interaktive Dienste konnte die Umsetzbarkeit dieses Konzepts demonstriert werden