Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

The final publication is available at Springer via http://dx.doi.org/DOI 10.1007/s10618-014-0378-6. Published online.Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.The work by C Saez has been supported by an Erasmus Lifelong Learning Programme 2013 Grant. This work has been supported by own IBIME funds. 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MUVTIME: a Multivariate time series visualizer for behavioral science

As behavioral science becomes progressively more data driven, the need is increasing for appropriate tools for visual exploration and analysis of large datasets, often formed by multivariate time series. This paper describes MUVTIME, a multimodal time series visualization tool, developed in Matlab that allows a user to load a time series collection (a multivariate time series dataset) and an associated video. The user can plot several time series on MUVTIME and use one of them to do brushing on the displayed data, i.e. select a time range dynamically and have it updated on the display. The tool also features a categorical visualization of two binary time series that works as a high-level descriptor of the coordination between two interacting partners. The paper reports the successful use of MUVTIME under the scope of project TURNTAKE, which was intended to contribute to the improvement of human-robot interaction systems by studying turn- taking dynamics (role interchange) in parent-child dyads during joint action.Marie Curie International Incoming Fellowship PIIF-GA-2011- 301155; Portuguese Foundation for Science and Technology (FCT) project PTDC/PSI- PCO/121494/2010; AFP was also partially funded by the FCT project (IF/00217/2013)This research was supported by: Marie Curie International Incoming Fellowship PIIF-GA-2011301155; Portuguese Foundation for Science and Technology (FCT) Strategic program FCT UID/EEA/00066/2013; FCT project PTDC/PSIPCO/121494/2010. AFP was also partially funded by the FCT project (IF/00217/2013). REFERENCE

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?

Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention. This trend has led researchers to compare only learning-based methods in their articles, abandoning some more conventional approaches. As a result, the community in this field has been encouraged to propose increasingly complex learning-based models mainly based on deep neural networks. To our knowledge, there are no comparative studies between conventional, machine learning-based and, deep neural network methods for the detection of anomalies in multivariate time series. In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets. By analyzing and comparing the performance of each of the sixteen methods, we show that no family of methods outperforms the others. Therefore, we encourage the community to reincorporate the three categories of methods in the anomaly detection in multivariate time series benchmarks

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac

Digging deep into weighted patient data through multiple-level patterns

Large data volumes have been collected by healthcare organizations at an unprecedented rate. Today both physicians and healthcare system managers are very interested in extracting value from such data. Nevertheless, the increasing data complexity and heterogeneity prompts the need for new efficient and effective data mining approaches to analyzing large patient datasets. Generalized association rule mining algorithms can be exploited to automatically extract hidden multiple-level associations among patient data items (e.g., examinations, drugs) from large datasets equipped with taxonomies. However, in current approaches all data items are assumed to be equally relevant within each transaction, even if this assumption is rarely true. This paper presents a new data mining environment targeted to patient data analysis. It tackles the issue of extracting generalized rules from weighted patient data, where items may weight differently according to their importance within each transaction. To this aim, it proposes a novel type of association rule, namely the Weighted Generalized Association Rule (W-GAR). The usefulness of the proposed pattern has been evaluated on real patient datasets equipped with a taxonomy built over examinations and drugs. The achieved results demonstrate the effectiveness of the proposed approach in mining interesting and actionable knowledge in a real medical care scenario

Molecular mechanisms of disiccation tolerance in Fucus

Intertidal algae (brown, red and green) are three understudied and independent multicellular lineages possessing related intolerant and desiccation tolerant species, making them good models for desiccation tolerance research. Recent focus on distribution of Fucus vesiculosus under climate change led us to determine the upper thermal limits of this brown algae, using physiological indicators and gene expression responses to describe the induction and thermal characteristics of the heat-shock response in diverse populations. Ambient temperatures were poor predictors of the heat-stress experienced by intertidal algae, instead the microhabitats created by the algal canopy modulated the local thermal environment and influenced the stress response. Surprisingly, in the hottest microhabitat algae appeared to be protected from thermal stress by fast and intense desiccation. Proteomic research in brown algae has recently been facilitated by genomic resources, complete genome sequencing of model species and large-scale transcriptomic resources from Fucus species, and by technical advances in work on organisms with similar interfering compounds. We tested and optimized a protein extraction protocol suitable for intertidal Fucus algae and used it to investigate differential expression of proteins in response to desiccation, both by conventional 2DE and by DIGE. No significant changes of the protein profiles were detected after desiccation or rehydration, suggesting the importance of constitutive tolerance mechanisms, minimizing the metabolic cost of gene expression, while the desiccated state provides protection against heat stress. Studies of distinct field environments (desiccation-prone or –protected), of sequential emersion stress exposure and of laboratory desiccation under controlled conditions, all failed to identify robust protein expression changes attributable to desiccation tolerance. We characterized the first extractable proteome of F. vesiculosus by LC-MS/MS identification and annotation against brown algal protein databases, with considerable success despite limited functional annotation in brown algae proteins, and the presence of multiple proteins in some spots.Na zona entre marés existem diversas macroalgas (castanhas, verdes e vermelhas), pertencentes a três linhagens multicelulares independentes e pouco estudadas, contendo espécies tolerantes e espécies intolerantes à dessecação, o que faz delas bons modelas para o estudo da tolerância à dessecação. Recentemente, a atenção dada à distribuição de Fucus vesiculosus sob efeito das alterações climáticas levou-nos a querer determinar os limites subletais máximos de temperatura desta alga castanha, utilizando indicadores fisiológicos e alterações da expressão genética para descrever a temperatura de indução e o perfil da resposta ao choque térmico em diversas populações desta espécie. Vimos que conhecer a temperatura ambiente não é suficiente para antecipar o choque térmico sentido pela alga, ao mesmo tempo que os microhabitats formados pelo tapete de algas vão influenciar a temperatura local e afectar a resposta ao choque térmico. Surpreendentemente, no microhabitat mais quente as algas aparentavam estar protegidas do choque térmico pela dessecação rápida e intensa. Estudos de proteómica em algas castanhas foram facilitados recentemente graças a recursos genéticos, a sequenciação do genoma completo da espécie modelo, Ectocarpus siliculosus, numerosas sequências de transcriptos de Fucus, obtidas pelas novas técnicas de sequenciação e avanços técnicos no estudo de outros organismos com compostos secundários semelhantes que interferem com a qualidade e a quantidade das proteínas extraídas. Foi optimizado um protocolo de extracção de proteínas de algas do género Fucus, que foi utilizado para investigar a expressão diferencial de proteínas em resposta à dessecação tanto por 2DE convencional como por 2D-DIGE. Não foram detectadas alterações significativas nos perfis de proteínas na sequência da dessecação ou da rehidratação, o que sugere a importância de mecanismos constitutivos de tolerância, minimizando os custos metabólicos da expressão de novos genes, enquanto a dessecação protege do choque térmico. Estudos de campo, em locais de intensa dessecação ou protegidos, após exposição consecutiva ao stress de emersão, e após dessecação em condições controladas no laboratório, todos falharam na identificação de alterações robustas na expressão de proteínas envolvidas na tolerância à dessecação. Caracterizámos o primeiro proteoma extraível de F. vesiculosus, identificando as proteínas por LC-MS/MS e anotando utilizando as bases de dados de algas castanhas. Esta anotação foi bem-sucedida, apesar da fraca anotação funcional das proteínas de algas castanhas e da presença de múltiplas proteínas em alguns dos spots

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709