Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications

The widespread dissemination of smart mobile devices enables new ways of collecting longitudinal data sets in a multitude of healthcare scenarios. On the one hand, mobile data collection can be accomplished more effectively and quicker compared with validated paper-based instruments. On the other hand, it can increase the data quality significantly and enable data collection in scenarios not covered by existing approaches so far. Previous attempts to utilize smart mobile devices for collecting data in these scenarios, however, often struggle with high costs for developing and maintaining mobile applications, which need to run on a multitude of mobile operating systems. Therefore, in the QuestionSys project, we are developing a generic (i.e., platform-independent) framework for enabling mobile data collection and sensor data integration in healthcare scenarios. The latter, in turn, is addressed by a model-driven approach, which is shown this paper along with the core components of the QuestionSys framework. In particular, it is shown how healthcare experts are empowered to create mobile data collection and sensing applications on their own and with reasonable efforts

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in Columbia Suicide Severity Rating Scale (C-SSRS), with the pairwise annotator agreement of 0.79 and group-wise agreement of 0.73. Compared to the existing four-label classification scheme (no risk, low risk, moderate risk, and high risk), our proposed C-SSRS-based 5-label classification scheme distinguishes people who are supportive, from those who show different severity of suicidal tendency. Our 5-label classification scheme outperforms the state-of-the-art schemes by improving the graded recall by 4.2% and reducing the perceived risk measure by 12.5%. Convolutional neural network (CNN) provided the best performance in our scheme due to the discriminative features and use of domain-specific knowledge resources, in comparison to SVM-L that has been used in the state-of-the-art tools over similar dataset

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in Columbia Suicide Severity Rating Scale (C-SSRS), with the pairwise annotator agreement of 0.79 and group-wise agreement of 0.73. Compared to the existing four-label classification scheme (no risk, low risk, moderate risk, and high risk), our proposed C-SSRS-based 5-label classification scheme distinguishes people who are supportive, from those who show different severity of suicidal tendency. Our 5-label classification scheme outperforms the state-of-the-art schemes by improving the graded recall by 4.2% and reducing the perceived risk measure by 12.5%. Convolutional neural network (CNN) provided the best performance in our scheme due to the discriminative features and use of domain-specific knowledge resources, in comparison to SVM-L that has been used in the state-of-the-art tools over similar dataset

Smart Service Innovation: Organization, Design, and Assessment

Background: The emergence of technologies such as the Internet of Things, big data, cloud computing, and wireless communication drives the digital transformation of the entire society. Organizations can exploit these potentials by offering new data-driven services with innovative value propositions, such as carsharing, remote equipment maintenance, and energy management services. These services result from value co-creation enabled by smart service systems, which are configurations of people, processes, and digital technologies. However, developing such systems was found to be challenging in practice. This is mainly due to the difficulties of managing complexity and uncertainty in the innovation process, as contributions of various actors from multiple disciplines must be coordinated. Previous research in service innovation and service systems engineering (SSE) has not shed sufficient light on the specifics of smart services, while research on smart service systems lacks empirical grounding. Purpose: This thesis aims to advance the understanding of the systematic development of smart services in multi-actor settings by investigating how smart service innovation (SSI) is conducted in practice, particularly regarding the participating actors, roles they assume, and methods they apply for designing smart service systems. Furthermore, the existing set of methods is extended by new methods for the design-integrated assessment of smart services and service business models. Approach: Empirical and design science methods were combined to address the research questions. To explore how SSI is conducted in practice, 25 interviews with experts from 13 organizations were conducted in two rounds. Building on service-dominant logic (SDL) as a theoretical foundation and a multi-level framework for SSI, the involvement of actors, their activities, employed means, and experienced challenges were collected. Additionally, a case study was used to evaluate the suitability of the Lifecycle Modelling Language to describe smart service systems. Design science methods were applied to determine a useful combination of service design methods and to build meta-models and tools for assessing smart services. They were evaluated using experiments and the talk aloud method. Results: On the macro-level, service ecosystems consist of various actors that conduct service innovation through the reconfiguration of resources. Collaboration of these actors is facilitated on the meso-level within a project. The structure and dynamics of project configurations can be described through a set of roles, innovation patterns, and ecosystem states. Four main activities have been identified, which actors perform to reduce uncertainty in the project. To guide their work, actors apply a variety of means from different disciplines to develop and document work products. The approach of design-integrated business model assessment is enabled through a meta-model that links qualitative aspects of service architectures and business models with quantitative assessment information. The evaluation of two tool prototypes showed the feasibility and benefit of this approach. Originality / Value: The results reported in this thesis advance the understanding of smart service innovation. They contribute to evidence-based knowledge on service systems engineering and its embedding in service ecosystems. Specifically, the consideration of actors, roles, activities, and methods can enhance existing reference process models. Furthermore, the support of activities in such processes through suitable methods can stimulate discussions on how methods from different disciplines can be applied and combined for developing the various aspects of smart service systems. The underlying results help practitioners to better organize and conduct SSI projects. As potential roles in a service ecosystem depend on organizational capabilities, the presented results can support the analysis of ex¬ternal dependencies and develop strategies for building up internal competencies.:Abstract iii Content Overview iv List of Abbreviations viii List of Tables x List of Figures xii PART A - SYNOPSIS 1 1 Introduction 2 1.1 Motivation 2 1.2 Research Objectives and Research Questions 4 1.3 Thesis Structure 6 2 Research Background 7 2.1 Smart Service Systems 7 2.2 Service-Dominant Logic 8 2.3 Service Innovation in Ecosystems 11 2.4 Systematic Development of Smart Service Systems 13 3 Research Approach 21 3.1 Research Strategy 21 3.2 Applied Research Methods 22 4 Summary of Findings 26 4.1 Overview of Research Results 26 4.2 Organizational Setup of Multi-Actor Smart Service Innovation 27 4.3 Conducting Smart Service Innovation Projects 32 4.4 Approaches for the Design-integrated Assessment of Smart Services 39 5 Discussion 44 5.1 Contributions 44 5.2 Limitations 46 5.3 Managerial Implications 47 5.4 Directions for Future Research 48 6 Conclusion 54 References 55 PART B - PUBLICATIONS 68 7 It Takes More than Two to Tango: Identifying Roles and Patterns in Multi-Actor Smart Service Innovation 69 7.1 Introduction 69 7.2 Research Background 72 7.3 Methodology 76 7.4 Results 79 7.5 Discussion 90 7.6 Conclusions and Outlook 96 7.7 References 97 8 Iterative Uncertainty Reduction in Multi-Actor Smart Service Innovation 100 8.1 Introduction 100 8.2 Research Background 103 8.3 Research Approach 109 8.4 Findings 113 8.5 Discussion 127 8.6 Conclusions and Outlook 131 8.7 References 133 9 How to Tame the Tiger – Exploring the Means, Ends, and Challenges in Smart Service Systems Engineering 139 9.1 Introduction 139 9.2 Research Background 140 9.3 Methodology 143 9.4 Results 145 9.5 Discussion and Conclusions 151 9.6 References 153 10 Combining Methods for the Design of Digital Services in Practice: Experiences from a Predictive Costing Service 156 10.1 Introduction 156 10.2 Conceptual Foundation 157 10.3 Preparing the Action Design Research Project 158 10.4 Application and Evaluation of Methods 160 10.5 Discussion and Formalization of Learning 167 10.6 Conclusion 169 10.7 References 170 11 Modelling of a Smart Service for Consumables Replenishment: A Life Cycle Perspective 171 11.1 Introduction 171 11.2 Life Cycles of Smart Services 173 11.3 Case Study 178 11.4 Discussion of the Modelling Approach 185 11.5 Conclusion and Outlook 187 11.6 References 188 12 Design-integrated Financial Assessment of Smart Services 192 12.1 Introduction 192 12.2 Problem Analysis 195 12.3 Meta-Model Design 200 12.4 Application of the Meta-Model in a Tool Prototype 204 12.5 Evaluation 206 12.6 Discussion 208 12.7 Conclusions 209 12.8 References 211 13 Towards a Cost-Benefit-Analysis of Data-Driven Business Models 215 13.1 Introduction 215 13.2 Conceptual Foundation 216 13.3 Methodology 218 13.4 Case Analysis 220 13.5 A Cost-Benefit-Analysis Model for DDBM 222 13.6 Conclusion and Outlook 225 13.7 References 226 14 Enabling Design-integrated Assessment of Service Business Models Through Factor Refinement 228 14.1 Introduction 228 14.2 Related Work 229 14.3 Research Goal and Method 230 14.4 Solution Design 231 14.5 Demonstration 234 14.6 Discussion 235 14.7 Conclusion 236 14.8 References 23

Validation Framework for RDF-based Constraint Languages

In this thesis, a validation framework is introduced that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, consists of a small lightweight vocabulary, and ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages

Estimating the family bias to autism: a bayesian approach

Autism is an age- and sex-related lifelong neurodevelopmental condition characterized pri marily by persistent deficits in core domains such as social communication. It is estimated that ≈ 2% of children have some ASD trait. The autism etiology is mainly due to inherited genetic factors (>80%). The importance of early diagnosis and interventions motivated several studies involving groups at high risk for ASD, those with a greater predisposition to the disorder. Such studies are characterized by evaluating some characteristics of the individual itself or the family members of diagnosed individuals, mainly aiming to predict a future diagnosis or recurrence rates. One of the primary goals of Artificial Intelligence is to create artificial agents capable of intelligent behaviors, such as prediction problems. Prediction problems usually involve reasoning with uncertainty due to some information deficiency, in which the data may be imprecise or incorrect. Such solutions may seek the application of probabilistic methods to construct inference models. In this thesis, we will discuss the development of probabilistic networks capable of estimating the risk of autism among the family members given some evidence (e.g., other family members with ASD). In particular, the main novel contributions of this thesis are as follows: the proposal of some estimates regarding parents with ASD generating children with ASD; the highlight ing regarding the decrease in the ASD prevalence sex ratio among males and females when genetic factors are taken into account; the corroboration and quantification of past evidence that the clustering of ASD in families is primarily due to genetic factors; the computation of some estimates regarding the risk of ASD for parents, grandparents, and siblings; an estimate regarding the number of ASD cases in a family sufficient to attribute the ASD occurrences to the genetic inheritance; the assessment of some estimates for males and females individuals given evidence in grandparents, aunts-or-uncles, nieces-or nephews and cousins; and the proposition of some estimates indicating risk ranges for ASD by genetic similarity