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  • 1. Hellström Karlsson, Rebecca
    et al.
    Shreenath, Vinutha Magal
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Aiding Remote Diagnosis with Text Mining2018In: Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018, 2018Conference paper (Refereed)
    Abstract [en]

    Along with the increase of digital healthcare providers, theinterest in diagnostic aids for remote diagnosis has increasedas well. As patients write about their symptoms themselves,we have access to a type of data which previously was rarelyrecorded, and which has not been filtered by a healthcareprofessional. Knowledge of similar patients and similarsymptoms is beneficial for doctors to arrive at a diagnosis.Therefore, the remote diagnostic process could be aided bypresenting patient cases together with information aboutsimilar patients and their self-reported symptom descriptions.Apart from online diagnosis, such an aid could bebeneficial in many healthcare settings, such as long-distancevisits and knowledge gain from patient diaries.In this paper, we present the impact of aiding remote diagnosisby presenting clusters of similar symptoms, usingsymptom descriptions collected from a virtual visit applicationby the Swedish telemedicine provider KRY. Symptomdescriptions were represented using the bag-of-words modeland were then clustered using the k-means algorithm. Anexperiment was then conducted with 13 doctors, where patientcases were presented together with the most representativewords of the associated cluster, to measure howtheir work was impacted. Results indicated that it was usefulin more complicated cases, but also that future experimentswill require further instructions on how the information is tobe interpreted.

  • 2.
    Raghothama, Jayanth
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Shreenath, Vinutha Magal
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Analytics on public transport delays with spatial big data2016In: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016, ACM Digital Library, 2016, p. 28-33Conference paper (Refereed)
    Abstract [en]

    The increasing pervasiveness of location-aware technologies is leading to the rise of large, spatio-temporal datasets and to the opportunity of discovering usable knowledge about the behaviors of people and objects. Applied extensively in transportation, spatial big data and its analytics can deliver useful insights on a number of different issues such as congestion, delays, public transport reliability and so on. Predominantly studied for its use in operational management, spatial big data can be used to provide insight in strategic applications as well, from planning and design to evaluation and management. Such large scale, streaming spatial big data can be used in the improvement of public transport, for example the design of public transport networks and reliability. In this paper, we analyze GTFS data from the cities of Stockholm and Rome to gain insight on the sources and factors influencing public transport delays in the cities. The analysis is performed on a combination of GTFS data with data from other sources. The paper points to key issues in the analysis of real time data, driven by the contextual setting in the two cities. © 2016, Association for Computing Machinery, Inc. All rights reserved.

  • 3. Shreenath, Vinutha Magal
    Creating Designs of Future Systems with Interpretation of Cognitive Artifacts in Reinforcement LearningIn: Design Science Journal, ISSN 2053-4701Article in journal (Refereed)
    Abstract [en]

    Designing future systems such as transport or healthcare in a city takes astute expertise. Design aids in such situations usually offer information in the form of projections or what-if analysis, using which experts make a series of decisions to create bounded designs. We present a case in which Reinforcement Learning (RL) is used to design the future transport system of a city. RL is used to create artifacts that reflect where the transport system can be changed. These agent-produced artifacts are then compared with designs made by human experts. This is achieved by analogizing the city as gridworld and using the same information that the human experts acted on as rewards. The interpretation of agent activity as cognitive artifacts of agents, along with measures of precision and recall to compare real and artificial artifacts form the basis of this work. This paper explores the use of RL in a real world context and the interpretability of results of RL with respect to design problems. The results indicate a robust initial approach to imitating expertise of designers and devising valid creativity in Socio-Technical Systems.

  • 4.
    Shreenath, Vinutha Magal
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
    Creating Knowledge with Data Science for Design in Systems2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Designing in large-scale engineering systems is a difficult cognitive task undertaken by experts. Knowledge of experts continually changes as they are confronted with similar by different problems in designing in such systems. However, it is also important that designers are presented information that is representative of the system,and that they are cognizant of activities on a system scale so they can create diverse choices for designs in early phase of design process.Data Science has been proven to be effective at informing people for decisions at immediate horizons. But the use of data science to drive long terms designs where experts have to make the right series of decisions i.e. designs is yet unknown. The use of data science is to inform decision makers of activities at system scale.In this thesis, I have looked at how data science can be used to create knowledge in designers for designing in large scale systems. I have also investigated further questions regarding imitation of expertise using AI, and in generating similar knowledge by creating diverse options in design.The results point out that data science can indeed inform designers, change their designs and hence create knowledge. They also point out that design cognition in experts can be partly imitated in data science itself, through careful modeling of the ill-defined problem in design. This therefore points to a promising future direction where data can be used as an interface between human thinking and machine learning, by translation of conceptual forms such as differential diagnoses and cognitive artefacts using data.

  • 5.
    Shreenath, Vinutha Magal
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Kornevs, Maksims
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Raghothama, Jayanth
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    A Feasibility Study for Gamification in Transport Maintenance: Requirements to implement gamification in heterogeneous organizations2015In: Games and Virtual Worlds for Serious Applications (VS-Games), 2015 7th International Conference on, IEEE conference proceedings, 2015, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Gamification has been successfully applied in many domains, but mostly for simple, isolated and operational tasks. The hope for gamification as a method to radically change and improve behavior, to provide incentives for sustained engagement has proven to be more difficult to get right. Applying gamification in large networked organizations with heterogeneous tasks remains a challenge. Applying gamification in such enterprise environments posits different requirements, and a match between these requirements and the institution needs to be investigated before venturing into the design and implementation of gamification. The current paper contributes a study where the authors investigate the feasibility of implementing gamification in Trafikverket, the Swedish transport administration. Through an investigation of the institutional arrangements around data collection, procurement processes and links to institutional structures, the study finds areas within Trafikverket where gamification could be successfully applied, and suggests gaps and methods to apply gamification in other areas.

  • 6.
    Shreenath, Vinutha Magal
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Imitating Design Cognition with Reinforcement Learning – Reflecting upon the Epistemology of Data ScienceIn: Minds and Machines, ISSN 0924-6495, E-ISSN 1572-8641Article in journal (Refereed)
    Abstract [en]

    The complexity of (re)design of large-scale systems in a metropolitan context is challenging its designers and their design methods. Data science has the potential to influence the designing process, but the question is to what extent and in what way. In this paper, we present an approach with reinforcement learning to imitate design cognition in a use case on Electric Road Systems, by acting on information produced by data science. This takes place in a loose game that is an abstract representation of the real system, thus artificially producing knowledge. We further discuss the episteme of that knowledge produced by the artificial.

  • 7.
    Shreenath, Vinutha Magal
    et al.
    KTH, School of Technology and Health (STH).
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering.
    Spatial Big Data for designing large scale infrastructure A case-study of Electrical Road Systems2016In: 2016 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES (BDCAT), IEEE , 2016, p. 143-148Conference paper (Refereed)
    Abstract [en]

    Decision making and planning of large scale infrastructures within cities is often a long process encompassing years, between multiple institutions represented by experts that require negotiations and consensus of demands and goals. The role big data plays in such design could be crucial, by providing access to otherwise elusive information on movements of people and goods in a city which can then transparently inform the design process, especially about possible demands and related complexities on the infrastructure being planned. To harness this data, it is necessary to formulate the problem technically such that data can inform experts, by articulating their expertise through the data. In this paper we present an application to analyze millions of instances of spatial data to identify potential locations for electrical road installation(s) in a city, to aid urban planners and other relevant stakeholders in planning and designing an Electrical Road System for a city. The dataset being used is gathered from a major vehicle manufacturer in Sweden, containing millions of instances of GPS data emitted by thousands of vehicles. A plan for electrified transport system is formulated by retrieving locations suitable for both static and dynamic charging installations. We investigate the technical formulation of methods and metrics for such a complex design problem, based on criteria set by experts, thus contributing to the science of big data for design of infrastructure and to methodology of data science in an institutional context.

  • 8.
    Shreenath, Vinutha Magal
    et al.
    KTH.
    Meijer, Sebastiaan
    KTH.
    Wyss, Ramon
    KTH.
    Kringos, Nicole
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
    C-CAMPUS: A PILOT STUDY ON CROSS-CULTURAL AND MULTI-DISCIPLINARY LEARNING2014In: EDULEARN14: 6TH INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES / [ed] Chova, LG Martinez, AL Torres, IC, IATED-INT ASSOC TECHNOLOGY EDUCATION A& DEVELOPMENT , 2014, p. 1296-1305Conference paper (Refereed)
    Abstract [en]

    Virtual learning environments are often associated with forms of virtual collaboration. Many studies have reported on the, often mixed, results of student collaborations from within the same population. Fewer studies have been done on cross-cultural collaboration platforms between geographically and culturally separated groups of students for learning more complex tasks. C-Campus is a cross-cultural and multi-disciplinary virtual environment with specifically tailored spaces for students to be able to learn, interact and socialize. In this environment the teachers and course developers can monitor and adjust the optimal learning conditions of the individual students. Students with diverse backgrounds from KTH Royal Institute of Technology in Sweden and Tsinghua University in China collaborated together on C-Campus for a course focusing on Future Highway Design. The structure of the course and the assignments were designed to encourage creative thinking, personal leadership and discussion. Students were split into diverse groups to work on projects with different perspectives on their Future Highway. The discussions among students took place in the space for creative learning on C-Campus. At the end of the course, they were asked to peer review the other members of their group and to assess their own performance during the course. This pilot provided an opportunity to observe individual student behaviour and group dynamics in a cross-cultural and multi-disciplinary setting. Data was gathered on all interactions in the groups, their teacher and mentor evaluations and the peer reviews among members in groups. An exit survey was also conducted on the ease of using the platform, on group activity and on the course itself. In this paper we describe the relation between the data gathered from students' interactions on C-Campus and the perceptions of performance by their peers and teachers. The data shows that interactions on C-Campus were a good indicator for the success of student learning and also to identify different types of behaviour. Having access to this type of data during the course can thus allow teachers to optimise learning for individual students and help nurture their abilities to work in multi-cultural and cross-disciplinary teams. The paper contributes both a unique cross-cultural learning case study, and more insights in the role of specific forms of data collection for monitoring of learning success in such environments.

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  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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  • text
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