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- ZeitschriftenartikelTowards Configuration Planning with Partially Ordered Preferences: Representation and Results(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Silva-Lopez, Lia Susana d. C.; Broxvall, Mathias; Loutfi, Amy; Karlsson, LarsConfiguration planning for a distributed robotic system is the problem of how to configure the system over time in order to achieve some causal and/or information goals. A configuration plan specifies what components (sensor, actuator and computational devices), should be active at different times and how they should exchange information. However, not all plans that solve a given problem need to be equally good, and for that purpose it may be important to take preferences into account. In this paper we present an algorithm for configuration planning that incorporates general partially ordered preferences. The planner supports multiple preference categories, and hence it solves a multiple-objective optimization problem: for a given problem, it finds all possible valid, non-dominated configuration plans. The planner has been able to successfully cope with partial ordering relations between quantitative preferences in practically acceptable times, as shown in the empirical results. Preferences here are represented as c-semirings, and are used for establishing dominance of a solution over another in order to obtain a set of configuration plans that will constitute the solution of a configuration planning problem with partially ordered preferences. The dominance operators tested in this paper are Pareto and Lorenz dominance. Our solver considers one guiding heuristic for obtaining the first solution, and then switches to a dominance based monotonically decreasing heuristic used for pruning dominated partial configuration plans. In our empirical results, we perform a statistical study in the space of problem instances and establish families of problems for which our approach is computationally feasible.
- ZeitschriftenartikelIs it Research or is it Spying? Thinking-Through Ethics in Big Data AI and Other Knowledge Sciences(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Berendt, Bettina; Büchler, Marco; Rockwell, Geoffrey“How to be a knowledge scientist after the Snowden revelations?” is a question we all have to ask as it becomes clear that our work and our students could be involved in the building of an unprecedented surveillance society. In this essay, we argue that this affects all the knowledge sciences such as AI, computational linguistics and the digital humanities. Asking the question calls for dialogue within and across the disciplines. In this article, we will position ourselves with respect to typical stances towards the relationship between (computer) technology and its uses in a surveillance society, and we will look at what we can learn from other fields. We will propose ways of addressing the question in teaching and in research, and conclude with a call to action.
- ZeitschriftenartikelA Paradigm Shift in Healthcare Provision(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Sonntag, Daniel; Gelissen, Jean
- ZeitschriftenartikelPersonalized Stress Management: Enabling Stress Monitoring with LifelogExplorer(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Kocielnik, Rafal; Sidorova, NataliaStress is one of the major triggers for many diseases. Improving stress balance is therefore an important prevention step. With advances in wearable sensors, it becomes possible to continuously monitor and analyse user’s behavior and arousal in an unobtrusive way. In this paper, we report on a case study in which users (21 teachers of a vocational school) were provided with wearable sensors and could view their arousal information put in the context of their life events during the period of four weeks using our software tool in an unsupervised setting. The goal was to evaluate user engagement and enabling of self-coaching abilities. Our results show that users actively explored their arousal data during the study. Further qualitative evaluation conducted with 15 of 21 users indicated that 12 of 15 users were able to learn about their stress patterns based on the information they obtained, but only 5 of them were able to come up with practical interventions for improving their stress balance on their own, while other users were of opinion that nothing can be done to reduce their stress, which suggests that self-coaching has some potential but there is need in further coaching support.
- ZeitschriftenartikelExploiting Latent Embeddings of Nominal Clinical Data for Predicting Hospital Readmission(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Krompaß, Denis; Esteban, Cristóbal; Tresp, Volker; Sedlmayr, Martin; Ganslandt, ThomasHospital readmissions of patients put a high burden not only on the health care system, but also on the patients since complications after discharge generally lead to additional burdens. Estimating the risk of readmission after discharge from inpatient care has been the subject of several publications in recent years. In those publications the authors mostly tried to directly infer the readmission risk (within a certain time frame) from the clinical data recorded in the medical routine such as primary diagnosis, co-morbidities, length of stay, or questionnaires. Instead of using these data directly as inputs for a prediction model, we are exploiting latent embeddings for the nominal parts of the data (e.g., diagnosis and procedure codes). These latent embeddings have been used with great success in the natural language processing domain and can be constructed in a preprocessing step. We show in our experiments, that a prediction model that exploits these latent embeddings can lead to improved readmission predictive models.
- ZeitschriftenartikelRecent Statistics on the Growth of the KI Journal(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Visser, Ubbo
- ZeitschriftenartikelSpecial Issue on Health and Wellbeing(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Gelissen, Jean; Sonntag, DanielThis special issue of KI Journal on health and wellbeing brings together a collection of articles on self-monitoring (quantified-self) related to health and habits (these solutions should reduce the costly demand for secondary prevention) and big health data analysis towards clinical data intelligence for individualized patient treatment. Others include invited contributions on intelligent user interfaces for health and wellbeing. The articles in this collection address diverse aspects of AI methods in the patient/user centric view, the doctor/clinical view, or the combination of these two views.
- ZeitschriftenartikelNow All Together: Overview of Virtual Health Assistants Emulating Face-to-Face Health Interview Experience(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Lisetti, Christine; Amini, Reza; Yasavur, UganWe discuss a large research project aimed at building socially expressive virtual health agents or assistants (VHA) that can deliver brief motivational interventions (BMI) for behavior change, in a communication style that individuals and patients not only accept, but also find emotionally supportive and socially appropriate. Because of their well-defined sequential structure, BMIs lend themselves well to automation, and are adaptable to address a variety of target behaviors, from obesity, to alcohol and drug use, to lack treatment adherence, among others. We discuss the advantages that VHAs provide for the delivery of health interventions. We describe components of our intelligent agent architecture that enables our virtual health agents to dialogue with users in realtime while delivering the appropriate intervention based on the patient’s specific needs at the time. We conclude by identifying open research challenges in developing virtual health agents.
- ZeitschriftenartikelSensemaking in Intelligent Health Data Analytics(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Boman, Magnus; Sanches, PedroA systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.
- ZeitschriftenartikelTechnology Roadmap Development for Big Data Healthcare Applications(KI - Künstliche Intelligenz: Vol. 29, No. 2, 2015) Zillner, Sonja; Neururer, SabrinaBig data applications indicate a wide range of opportunities to improve the overall quality and efficiency of healthcare delivery. The highest impact of big data applications is expected when data from various healthcare areas, such as clinical, administrative, financial, or outcome data, can be integrated. However, as of today, the realization of big data healthcare applications aggregating various kinds of data sources is still lacking behind. In order to foster the implementation of comprehensive big data applications, a clear understanding of short-term and long-term goals of envisioned big data scenarios is needed to forecast which emerging big data technologies are needed at what point in time. The contribution of this paper is to introduce the development of a technology roadmap for big data technologies in the healthcare domain. Beside the description of user needs and the technologies needed in order to satisfy those needs, the technology roadmap provides a basis to forecast technology developments and, thus, guidance in planning and coordinating technology developments accordingly.