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Opening the black box of electronic health: Collecting, analyzing, and interpreting log data

JMIR research Protocols 2017年第8期6卷 e156页

作者： Sieverink, Floor Kelders, Saskia Poel, Mannes van Gemert-Pijnen, Lisette Centre for eHealth and Wellbeing Research Department of Psychology Health and Technology University of Twente Enschede Netherlands Optentia Research Focus Area North-West University Vanderbijlpark South Africa Human Media Interaction Group Department of Computer Science University of Twente Enschede Netherlands

In electronic health (eHealth) research, limited insight has been obtained on process outcomes or how the use of technology has contributed to the users' ability to have a healthier life, improved well-being, or activate new attitudes in their daily tasks. As a result, eHealth is often perceived as a black box. To open this black box of eHealth, methodologies must extend beyond the classic effect evaluations. The analyses of log data (anonymous records of real-time actions performed by each user) can provide continuous and objective insights into the actual usage of the technology. However, the possibilities of log data in eHealth research have not been exploited to their fullest extent. The aim of this paper is to describe how log data can be used to improve the evaluation and understand the use of eHealth technology with a broader approach than only descriptive statistics. This paper serves as a starting point for using log data analysis in eHealth research. Here, we describe what log data is and provide an overview of research questions to evaluate the system, the context, the users of a technology, as well as the underpinning theoretical constructs. We also explain the requirements for log data, the starting points for the data preparation, and methods for data collection. Finally, we describe methods for data analysis and draw a conclusion regarding the importance of the results for both scientific and practical applications. The analysis of log data can be of great value for opening the black box of eHealth. A deliberate log data analysis can give new insights into how the usage of the technology contributes to found effects and can thereby help to improve the persuasiveness and effectiveness of eHealth technology and the underpinning behavioral models. © Floor Sieverink, Saskia Kelders, Mannes Poel, Lisette van Gemert-Pijnen. Originally published in JMIR research Protocols (http://***), 07.08.2017. This is an open-access article distribu

关键词： Black box EHealth Evaluation Log data analysis

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Making a case for electronic lectures

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Innovations in Education and Teaching International 1997年第1期34卷 11-16页

作者： Barker, Philip Tan, Check Meng University of Teesside United Kingdom Interactive Systems Research Group United Kingdom Human-Computer Interaction Laboratory School of Computing and Mathematics University of Teesside Middlesbrough TS1 3BA Borough Road United Kingdom

The use of electronic resources for the support of teaching and learning activities is growing very rapidly. A major advantage of having pedagogic materials in this form is that they can easily be 'recycled' to fulfil a number of different functions. This paper discusses how electronic lectures can be used to improve conventional lecture-based presentations and how they can then form the basis for the development of self-study material for the support of open and distance learning.

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group-in-a-Box Layout for Multi-faceted Analysis of Communities

Group-in-a-Box Layout for Multi-faceted Analysis of Communit...

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IEEE International Conference on and IEEE International Confernece on Social Computing (SocialCom) Privacy, Security, Risk and Trust (PASSAT)

作者： Eduarda Mendes Rodrigues Natasa Milic-Frayling Marc Smith Ben Shneiderman Derek Hansen Department of Informatics Engineering Faculty of Engineering University of Porto Portugal Microsoft Research Cambridge UK Connected Action Consulting Group Belmont CA USA Department of Computer Science & Human-Computer Interaction Lab University of Maryland College Park MD USA College of Information Studies & Center of the Advanced Study of Communities and Information University of Maryland College Park MD USA

Communities in social networks emerge from interactions among individuals and can be analyzed through a combination of clustering and graph layout algorithms. These approaches result in 2D or 3D visualizations of clustered graphs, with groups of vertices representing individuals that form a community. However, in many instances the vertices have attributes that divide individuals into distinct categories such as gender, profession, geographic location, and similar. It is often important to investigate what categories of individuals comprise each community and vice-versa, how the community structures associate the individuals from the same category. Currently, there are no effective methods for analyzing both the community structure and the category-based partitions of social graphs. We propose group-In-a-Box (GIB), a meta-layout for clustered graphs that enables multi-faceted analysis of networks. It uses the tree map space filling technique to display each graph cluster or category group within its own box, sized according to the number of vertices therein. GIB optimizes visualization of the network sub-graphs, providing a semantic substrate for category-based and cluster-based partitions of social graphs. We illustrate the application of GIB to multi-faceted analysis of real social networks and discuss desirable properties of GIB using synthetic datasets.

关键词： Layout Communities Clustering algorithms Visualization Algorithm design and analysis Social network services Image edge detection

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FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection

arXiv

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arXiv 2024年

作者： Gutwein, Simon Kampel, Martin Taschner-Mandl, Sabine Licandro, Roxane St. Anna Children’s Cancer Research Institute Vienna Austria TU Wien Institute of Visual Computing and Human-Centered Technology Computer Vision Lab Vienna Austria Medical University of Vienna Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research ELIA Group Vienna Austria

Detecting genetic aberrations is crucial in cancer diagnosis, typically through fluorescence in situ hybridization (FISH). However, existing FISH image classification methods face challenges due to signal variability, the need for costly manual annotations and fail to adequately address the intrinsic uncertainty. We introduce a novel approach that leverages synthetic images to eliminate the requirement for manual annotations and utilizes a joint contrastive and classification objective for training to account for inter-class variation effectively. We demonstrate the superior generalization capabilities and uncertainty calibration of our method, which is trained on synthetic data, by testing it on a manually annotated dataset of real-world FISH images. Our model offers superior calibration in terms of classification accuracy and uncertainty quantification with a classification accuracy of 96.7% among the 50% most certain cases. The presented end-to-end method reduces the demands on personnel and time and improves the diagnostic workflow due to its accuracy and adaptability. All code and data is publicly accessible at: https://***/SimonBon/FISHing. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

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Greedy information acquisition algorithm: a new information theoretic method for network growing

Greedy information acquisition algorithm: a new information ...

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International Joint Conference on Neural Networks (IJCNN)

作者： R. Kamimura T. Kamimura O. Uchida T. Takeuchi Information Science Laboratory and Future Science and Technology Joint Research Center Tokai University Hiratsuka Kanagawa Japan Department of English Senshu University Kawasaki Kanagawa Japan Department of Network Engineering Kanagawa Institute of Technology Atsugi Kanagawa Japan Human-Computer Interaction Group Institute for Human Science and Biomedical Engineering National Institute for Advanced Industrial Science and Technology Tsukuba Japan

In this paper, we propose a new information theoretic network growing algorithm. The new approach is called greedy information acquisition, because networks try to absorb as much information as possible in every stage of learning. In the first stage, two competitive units compete with each other by maximizing mutual information. In the successive stages, new competitive units are gradually added and information is maximized. Through greedy information maximization, different sets of important features in input patterns can be cumulatively discovered in successive stages. We applied our approach to a language classification problem. Experimental results confirmed that different features in input patterns are gradually discovered.

关键词： Biomedical engineering Information science humans Mutual information Neural networks Neurons

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Fifth international workshop on Behavior Change Support Systems (BCSS 2017)

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CEUR Workshop Proceedings 2017年 1822卷 1-6页

作者： Tikka, Piiastiina Klaassen, Randy Karppinen, Pasi Van Delden, Robby De Vries, Roelof Van Gemert-Pijnen, Lisette Oinas-Kukkonen, Harri Heylen, Dirk Oulu Advanced Research on Service and Information Systems Group Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Human Media Interaction Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Psychology Health and Technology Faculty of Behavioural Management and Social Sciences University of Twente Enschede Netherlands

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Data recording in primary care field studies: Patient records enhancement project

Data recording in primary care field studies: Patient record...

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International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth

作者： Lesley Axelrod Flis Henwood Geraldine Fitzpatrick Jackie Cassell Helen Smith Amanda Nicholson Greta Rait Human Centred Technology Laboratory Interactive Systems Group School of Informatics University of Sussex Sussex UK Human-Computer Interaction Group Technical University of Vienna Vienna Austria Social Informatics Research Unit School of Applied Social Science University of Brighton Brighton Sussex UK Brighton and Sussex Medical School Brighton Sussex UK Department of Primary Care and Population Health University College London Medical School London UK

This position paper describes the human-computer interaction (HCI) field studies component of the multidisciplinary Patient Records Enhancement Project (PREP). PREP seeks to understand variability of data found in primary care electronic records, in particular the balance between coded data and doctor's 'free text' notes. HCI fieldwork will establish variables that affect recording practices. In field studies we observe and record data recording practices in general practice (GP) surgeries, interview staff, video consultations with real patients and video consultations with standardized patients (played by medical actors). By standardizing patients we can compare the impact of other variables: different doctors, in different surgeries, using different e-health systems. Our early findings suggest that variability is due to a complex web of reasons, driven by personal, contextual and organizational processes. Findings from thematic analysis will result in design implications for studies by epidemiologists and public health researchers, design of NHS training and work processes, and design of electronic health record interfaces.

关键词： Surgery Educational institutions Medical diagnostic imaging human computer interaction Diseases Databases

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Towards a shared definition of user experience

Towards a shared definition of user experience

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28th Annual CHI Conference on human Factors in Computing Systems

作者： Law, Effie Roto, Virpi Vermeeren, Arnold P. O. S. Kort, Joke Hassenzahl, Marc ETH Zürich Institut TIK 8092 Zurich Switzerland Univ. of Leicester Dept. of CS LE1 7RH Leicester United Kingdom Nokia Research Center P.O.Box 407 00045 Nokia Group Finland Delft University of Technology Department of Industrial Design Landbergstraat 15 2628 CE Delft Netherlands TNO Information and Communication Technology P.O. Box 1416 9701 BK Groningen Netherlands Economic Psychology and Human-Computer-Interaction University of Koblenz-Landau Fortstraße 7 76829 Landau Germany

User experience (UX) is still an elusive notion with many different definitions, despite some recent attempts to develop a unified view on UX. The lack of a shared definition of UX not only confuses or even misleads customers of a product/service but also undermines the effectiveness of researching, managing and teaching UX. Diverse ideas have been generated in scientific activities that aim to develop a common understanding about the meaning and scope of UX. It is plausible, with sound methodologies, to converge these divergences, driving the UX community closer to a common definition and integrated views of UX. This SIG tackles this challenge by systematically assembling a set of existing definitions and viewpoints of UX and collecting opinions on them from known UX experts/researchers and general CHI'08 attendees.

关键词： human engineering

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Data recording in primary care field studies: Patient records enhancement project

Data recording in primary care field studies: Patient record...

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2011 5th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2011

作者： Axelrod, Lesley Henwood, Flis Fitzpatrick, Geraldine Cassell, Jackie Smith, Helen Nicholson, Amanda Rait, Greta Human Centred Technology Lab School of Informatics University of Sussex Falmer Sussex BN1 9QH United Kingdom Social Informatics Research Unit School of Applied Social Science University of Brighton Falmer Brighton Sussex BN1 9PH United Kingdom Human-Computer Interaction Group Technical University of Vienna Karlsplatz 13 1040 Vienna Austria Brighton and Sussex Medical School Mayfield House Falmer Brighton Sussex BN1 9PH United Kingdom Department of Primary Care and Population Health University College London Medical School London NW3 2NF United Kingdom

ISBN: (纸本)9781936968152

关键词： human computer interaction

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human-Robot interaction 1

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丛书名： Springer Series on Bio- and Neurosystems

1000年

作者： Céline Jost Brigitte Le Pévédic Tony Belpaeme Cindy Bethel Dimitrios Chrysostomou Nigel Crook Marine Grandgeorge Nicole Mirnig

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