An Overview of Computational Approaches for Interpretation Analysis

It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an image that makes person A regard an image as beautiful while person B finds the same image displeasing? Such questions ultimately aim at explaining our individual ways of interpretation, an intention that has been of fundamental importance to the social sciences from the beginning. More recently, advances in computer science brought up two related questions: First, can computational tools be adopted for analyzing ways of interpretation? Second, what if the "beholder" is a computer model, i.e., how can we explain a computer model's point of view? Numerous efforts have been made regarding both of these points, while many existing approaches focus on particular aspects and are still rather separate. With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. We give an overview of relevant computational approaches from various fields, and discuss the most common and promising application areas. The focus of this paper lies on interpretation of text and image data, while many of the presented approaches are applicable to other types of data as well.Comment: Preprint submitted to Digital Signal Processin

Process Evaluation of a Dutch Community Intervention to improve Health Related Behaviour in deprived neighbourhoods

Objectives: To assess whether a community intervention on health related behaviour in deprived neighbourhoods was delivered as planned and the extent of exposure to the intervention programme. Methods: Data were gathered throughout the intervention period using minutes of meetings, registration forms and a postal questionnaire among residents in intervention and comparison neighbourhoods. Results: Overall, the intervention was delivered according to the key principles of a "community approach", although community participation could have been improved. Neighbourhood coalitions organized more than 50 health related activities in the neighbourhoods over a two-year period. Most activities were directed at attracting attention, providing information, and increasing awareness and knowledge, and at changing behaviours. Programme awareness and programme participation were 24% respectively 3% among residents in the intervention neighbourhoods. Conclusions: The process evaluation indicated that it was feasible to implement a community intervention according to the key principles of the "community approach" in deprived neighbourhoods. However, it is unlikely that the total package of intervention activities had enough strength and sufficient exposure to attain community-wide health behaviour change

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutionspublishedVersio

Sociocultural and psychological determinants in migrants for noncompliance with occlusion therapy for amblyopia

Background: Compliance with occlusion therapy for amblyopia in children is low when their parents have a low level of education, speak Dutch poorly, or originate from another country. We determined how sociocultural and psychological determinants affect compliance. Methods: Included we

Consensus on a conversation aid for shared decision making with people with intellectual disabilities in the palliative phase

Background: Little is known about how to involve people with intellectual disabilities in making decisions about treatment and care in their palliative phase. We aimed to reach a consensus about a shared decision-making (SDM) conversation aid for people with intellectual disabilities, relatives, and healthcare professionals. Methods: In a Delphi process, an expert panel of 11 people with intellectual disabilities, 14 relatives, and 65 healthcare profe