PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

(no abstract

MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework

As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data. Thus, in this paper, a deep learning-based python package for data integration is developed. The python package deep learning-based multimodal longitudinal data integration framework (MildInt) provides the preconstructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved. We validated the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer's disease to predict the disease progression. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset

Barry Smith an sich

Festschrift in Honor of Barry Smith on the occasion of his 65th Birthday. Published as issue 4:4 of the journal Cosmos + Taxis: Studies in Emergent Order and Organization. Includes contributions by Wolfgang Grassl, Nicola Guarino, John T. Kearns, Rudolf Lüthe, Luc Schneider, Peter Simons, Wojciech Żełaniec, and Jan Woleński

Clinical governance, education and learning to manage health information

Purpose – This paper aims to suggest that the concept of clinical governance goes beyond a bureaucratic accountability structure and can be viewed as a negotiated balance between imperfectly aligned and sometimes conflicting goals within a complex adaptive system. On this view, the information system cannot be separated conceptually from the system of governance it supports or the people whose work it facilitates or hinders. Design/methodology/approach – The study, located within the English National Health Service (NHS) between 1999 and 2005, is case study based using a multi method approach to data collection within two primary care organisations (PCOs). The research strategy is conducted within a social constructionist ontological perspective. Findings – The findings reflect the following broad-based themes: mutual adjustment of a plurality of stakeholder perceptions, preferences and priorities; the development of information and communication systems, empowered by informatics; an emphasis on education and training to build capacity and capability. Research limitations/implications – Limitations of case study methodology include a tendency to provide selected accounts. These are potentially biased and risk trivialising findings. Rooted in specific context, their generalisability to other contexts is limited by the extent to which contexts are similar. Reasonable attempts were made to minimise any bias. The diversity of data collection methods used in the study was an attempt to counterbalance the limitations highlighted in one method by strength from alternative techniques. Practical implications – The paper makes recommendations in two key governance areas: education and learning to manage health information. In practice, the lessons learned provide opportunities to inform future approaches to health informatics educational programmes. Originality/value – With regard to topicality, it is suggested that many of the developmental issues highlighted during the establishment of quality improvement programmes within primary care organisations (PCGs/PCTs) are relevant in the light of current NHS reforms and move towards commissioning consortia

Nursing Students\u27 Self-Efficacy and Attitude: Examining the Influence ofthe Omaha System In Nurse Managed Centers

Self-efficacy, or confidence, as an outcome behavior has been identified as influencing nursing job satisfaction and retention. Clinical learning environments and teaching strategies that build and support perceived self-efficacy are critical aspects of preparing new nurses for their entry and continuing role as professional nurses in today\u27s information-intensive data-management healthcare environment. The purpose of this pre-test post-test study is to measure, using the C-scale (Grundy, 1992), nursing students\u27 self-efficacy to perform patient assessment in Nurse Managed Centers (NMC) after one semester of using the Omaha System documentation framework. Nursing students\u27 attitudes of preparation for using Standardized Nursing Languages (SNL) in the future was also examined. Bandura\u27s (1977, 19986) theoretical model of self-efficacy provided the conceptual framework. Students\u27 overall self-efficacy scores increased significantly over the 12 week study. Use of the Omaha System \u27prepared a little\u27 to \u27very prepared\u27 90% of student nurses for future use of SNL. Continued use of the Omaha System documentation framework in Nurse Managed Center clinicals as a tool for understanding SNL is recommended.