User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Bringing Anglo-governmentality into public management scholarship : the case of evidence-based medicine in UK health care

The field of public administration and management exhibits a limited number of favored themes and theories, including influential New Public Management and Network Governance accounts of contemporary government. Can additional social science–based perspectives enrich its theoretical base, in particular, analyzing a long-term shift to indirect governance evident in the field? We suggest that a variant of Foucauldian analysis is helpful, namely “Anglo-governmentality.” Having reviewed the literatures, we apply this Anglo-governmentality perspective to two case studies of “post hierarchical” UK health care settings: first, the National Institute for Health and Clinical Excellence (NICE), responsible for producing evidence-based guidelines nationally, and the second, a local network tasked with enacting such guidelines into practice. Compared with the Network Governance narrative, the Anglo-governmentality perspective distinctively highlights (a) a power–knowledge nexus giving strong technical advice; (b) pervasive grey sciences, which produce such evidence-based guidelines; (c) the “subjectification” of local governing agents, herein analyzed using Foucauldian concepts of the “technology of the self” and “pastoral power”; and (d) the continuing indirect steering role of the advanced neoliberal health care State. We add to Anglo-governmentality literature by highlighting hybrid “grey sciences,” which include clinical elements and energetic self-directed clinical–managerial hybrids as local governing agents. These findings suggest that the State and segments of the medical profession form a loose ensemble and that professionals retain scope for colonizing these new arenas. We finally suggest that Anglo-governmentality theory warrants further exploration within knowledge-based public organizations