Integration of linked open data in case-based reasoning systems

This paper discusses the opportunities of integrating Linked Open Data (LOD) resources into Case-Based Reasoning (CBR) systems. Upon the application domain travel medicine, we will exemplify how LOD can be used to fill three out of four knowledge containers a CBR system is based on. The paper also presents the applied techniques for the realization and demonstrates the performance gain of knowledge acquisition by the use of LOD

When to Explain? Model Agnostic Explanation Using a Case-based Approach and Counterfactuals

ExplainableArtificialIntelligence(XAI)systemshavegained importance with the increasing demand for understanding why and how an artificial intelligence system makes decisions. Counterfactual expla- nations, one of the rising trends of XAI, benefit from human counter- factual thinking mechanisms and aim to follow a similar way of rea- soning. In this paper, we create an eXplainable Case-Based Reasoning system using counterfactual samples with a model-agnostic approach. While CBR methodology allows us to use past experiences to create new explanations, using counterfactuals helps to increase understandability. The main idea of this paper is to generate an explanation when necessary. The proposed method is sample-centric. Thus, an adaptive explanation area is calculated for each data point in the dataset. We detect if there is any existing counterfactual of the samples to increase the coverage of the system, and we create explanation cases from detected sample- counterfactual pairs. If a query case is in the explanation area, at least one explanation case will be triggered, and a two-phase explanation will be created using a text template and a bi-directional bar graph. In this work, we will show (1) how explanation cases are created, (2) how the nature of a dataset influences the explanation area, (3) how understand- able explanations are created, and (4) how the proposed method works on open datasets

Knowledge modelling with the open source tool myCBR

Building knowledge intensive Case-Based Reasoning applications requires tools that support this on-going process between domain experts and knowledge engineers. In this paper we will introduce how the open source tool myCBR 3 allows for flexible knowledge elicitation and formalisation form CBR and non CBR experts. We detail on myCBR 3 's versatile approach to similarity modelling and will give an overview of the Knowledge Engineering workbench, providing the tools for the modelling process. We underline our presentation with three case studies of knowledge modelling for technical diagnosis and recommendation systems using myCBR 3

Deriving case base vocabulary from web community data

This paper presents and approach for knowledge extraction for Case-Based Reasoning systems. The recent development of the WWW, especially the Web 2.0, shows that many successful applications are web based. Moreover, the Web 2.0 offers many experiences and our approach uses those experiences to fill the knowledge containers. We are especially focusing on vocabulary knowledge and are using forum posts to create domain-dependent taxonomies that can be directly used in Case-Based Reasoning systems. This paper introduces the applied knowledge extraction process based on the KDD process and explains its application on a web forum for travelers

Learning to recognise exercises for the self-management of low back pain.

Globally, Low back pain (LBP) is one of the top three contributors to years lived with disability. Self-management with an active lifestyle is the cornerstone for preventing and managing LBP. Digital interventions are introduced in the recent past to improve and reinforce self-management where regular exercises are a core component and they rely on self-reporting to keep track of exercises performed. This data directly influences the recommendations made by the digital intervention where accurate and reliable reporting is fundamental to the success of the intervention. In addition, performing exercises with precision is important where current systems are unable to provide the guidance required. The main challenge to implementing an end-to-end solution is the lack of public sensor-rich datasets to implement Machine Learning algorithms to perform Exercise Recognition (ExR) and qualitative analysis. Accordingly we introduce the ExR benchmark dataset “MEx”, which we have shared publicly to encourage furthering research. In this paper we benchmark state-of-the art classification algorithms with deep and shallow architectures on each sensor and achieve performance up to 90.2%. We recognise the scope of each sensor in capturing exercise movements with confusion matrices and highlight the most suitable sensors for deployment considering performance vs. obtrusiveness

SEASALTexp - an explanation-aware architecture for extracting and case-based processing of experiences from internet communities

This paper briefly describes SEASALTexp, an extension of the application-independent SEASALT architecture (Sharing Experience using an Agent-based explanation-aware System Architecture LayouT), which offers knowledge acquisition from Internet communities, knowledge modularisation, and agent-based knowledge maintenance complemented with agent-based explanation facilities

Effectiveness of app-delivered, tailored self-management support for adults with lower back pain-related disability: a selfBACK randomized clinical trial.

Importance: Lower back pain (LBP) is a prevalent and challenging condition in primary care. The effectiveness of an individually tailored self-management support tool delivered via a smartphone app has not been rigorously tested. Objective: To investigate the effectiveness of selfBACK, an evidence-based, individually tailored self-management support system delivered through an app as an adjunct to usual care for adults with LBP-related disability. Design, Setting, and Participants: This randomized clinical trial with an intention-to-treat data analysis enrolled eligible individuals who sought care for LBP in a primary care or an outpatient spine clinic in Denmark and Norway from March 8 to December 14, 2019. Participants were 18 years or older, had nonspecific LBP, scored 6 points or higher on the Roland-Morris Disability Questionnaire (RMDQ), and had a smartphone and access to email. Interventions: The selfBACK app provided weekly recommendations for physical activity, strength and flexibility exercises, and daily educational messages. Self-management recommendations were tailored to participant characteristics and symptoms. Usual care included advice or treatment offered to participants by their clinician. Main Outcomes and Measures: Primary outcome was the mean difference in RMDQ scores between the intervention group and control group at 3 months. Secondary outcomes included average and worst LBP intensity levels in the preceding week as measured on the numerical rating scale, ability to cope as assessed with the Pain Self-Efficacy Questionnaire, fear-avoidance belief as assessed by the Fear-Avoidance Beliefs Questionnaire, cognitive and emotional representations of illness as assessed by the Brief Illness Perception Questionnaire, health-related quality of life as assessed by the EuroQol-5 Dimension questionnaire, physical activity level as assessed by the Saltin-Grimby Physical Activity Level Scale, and overall improvement as assessed by the Global Perceived Effect scale. Outcomes were measured at baseline, 6 weeks, 3 months, 6 months, and 9 months. Results: A total of 461 participants were included in the analysis; the population had a mean [SD] age of 47.5 [14.7] years and included 255 women (55%). Of these participants, 232 were randomized to the intervention group and 229 to the control group. By the 3-month follow-up, 399 participants (87%) had completed the trial. The adjusted mean difference in RMDQ score between the 2 groups at 3 months was 0.79 (95% CI, 0.06-1.51; P=.03), favoring the selfBACK intervention. The percentage of participants who reported a score improvement of at least 4 points on the RMDQ was 52% in the intervention group vs 39% in the control group (adjusted odds ratio, 1.76; 95% CI, 1.15-2.70; P=.01). Conclusions and Relevance: Among adults who sought care for LBP in a primary care or an outpatient spine clinic, those who used the selfBACK system as an adjunct to usual care had reduced pain-related disability at 3 months. The improvement in pain-related disability was small and of uncertain clinical significance. Process evaluation may provide insights into refining the selfBACK app to increase its effectiveness. Trial Registration: ClinicalTrials.gov Identifier: NCT03798288

Design of a clinician dashboard to facilitate co-decision making in the management of non-specific low back pain

This paper presents the design of a Clinician Dashboard to promote co-decision making between patients and clinicians. Targeted patients are those with non-specific low back pain, a leading cause of discomfort, disability and absence from work throughout the world. Targeted clinicians are those in primary care, including general practitioners, physiotherapists, and chiropractors. Here, the functional specifications for the Clinical Dashboard are delineated, and wireframes illustrating the system interface and flow of control are shown. Representative scenarios are presented to exemplify how the system could be used for co-decision making by a patient and clinician. Also included are a discussion of potential barriers to implementation and use in clinical practice and a look ahead to future work. This work has been conducted as part of the Horizon 2020 selfBACK project, which is funded by the European Commission

RADAR à la carte: Vom generischen Forschungsdatenrepository bis zum fachspezifischen Einsatz

Digitale Forschungsdaten sicher archivieren und publizieren zu können, sie auffindbar, zugänglich, nachnutzbar und zitierfähig zu machen, war und ist das Ziel des 2017 gestarteten Datenrepositorys RADAR[1]. Das im Rahmen eines DFG-Projekts entwickelte System wird von FIZ Karlsruhe – Leibniz-Institut für Informationsinfrastruktur betrieben und als generischer Cloud-Dienst von derzeit annähernd 20 Hochschulen und außeruniversitären Forschungseinrichtungen genutzt. Seit Inbetriebnahme von RADAR haben sich das Umfeld für Forschungsdatenrepositorys und auch die Anforderungen von Forschenden und nutzenden Einrichtungen dynamisch entwickelt. Dabei wurde für FIZ Karlsruhe offensichtlich, dass die bisherige Fokussierung von RADAR auf das disziplinunabhängige Cloud-Angebot zu kurz greift und nur mit zusätzlichen Betriebsvarianten für Institutionen (z.B. RADAR Local[2]) und Dienstangeboten für neue Zielgruppen eine ausreichend große Nutzerbasis geschaffen werden kann, um das System nachhaltig zu betreiben. So adressiert RADAR zwischenzeitlich mit communityspezifischen Angeboten neue Nutzergruppen, insbesondere im Rahmen der Nationalen Forschungsdateninfrastruktur (NFDI[3]). Mit RADAR4Culture[4] und RADAR4Chem[5] sind seit Anfang 2022 niedrigschwellige und kostenfreie Datenpublikationsangebote für Forschende im Umfeld der Fachkonsortien Chemie (NFDI4Chem) sowie materieller und immaterieller Kulturgüter (NFDI4Culture) verfügbar. Weitere vergleichbare Angebote zur nachhaltigen Veröffentlichung von Forschungsdaten in anderen Wissenschaftscommunitys mit noch nicht vorhandener oder unzureichender Forschungsdateninfrastruktur sind in Vorbereitung. Speziell in diesen fachspezifischen Kontexten ist jedoch auch eine Öffnung und Flexibilisierung des etablierten generischen RADAR-Metadatenschemas notwendig: zusätzlich zum disziplinagnostischen Annotationsansatz (10 Pflichtfelder / 13 optionale Felder) wird RADAR zukünftig auch Annotationen nach disziplineigenen Bedürfnissen nutzerfreundlich unterstützen und konfigurierbar machen. Hierfür wird aktuell ein spezieller „RADAR-Metadatenservice“ entwickelt, mit dessen Hilfe communityspezifische bzw. individuelle Metadatenschemata menügesteuert erstellt und die entsprechenden Metadatenannotationen bequem per Eingabemaske durchgeführt werden können. Unser Poster beleuchtet die genannten Neuerungen, mit denen sich RADAR flexibel für den Einsatz durch wissenschaftliche Fachcommunitys für die Zukunft positioniert. [1] RADAR steht für Research Data Repository: https://www.radar-service.eu [2] RADAR Local: https://radar.products.fiz-karlsruhe.de/de/radarvariants/betriebsvarianten#radar+local [3] NFDI: https://www.nfdi.de [4] RADAR4Culture: https://radar.products.fiz-karlsruhe.de/de/radarabout/radar4culture [5] RADAR4Chem: https://radar.products.fiz-karlsruhe.de/de/radarabout/radar4che