MuCIGREF: multiple computer-interpretable guideline representation and execution framework for managing multimobidity care

Clinical Practice Guidelines (CPGs) supply evidence-based recommendations to healthcare professionals (HCPs) for the care of patients. Their use in clinical practice has many benefits for patients, HCPs and treating medical centres, such as enhancing the quality of care, and reducing unwanted care variations. However, there are many challenges limiting their implementations. Initially, CPGs predominantly consider a specific disease, and only few of them refer to multimorbidity (i.e. the presence of two or more health conditions in an individual) and they are not able to adapt to dynamic changes in patient health conditions. The manual management of guideline recommendations are also challenging since recommendations may adversely interact with each other due to their competing targets and/or they can be duplicated when multiple of them are concurrently applied to a multimorbid patient. These may result in undesired outcomes such as severe disability, increased hospitalisation costs and many others. Formalisation of CPGs into a Computer Interpretable Guideline (CIG) format, allows the guidelines to be interpreted and processed by computer applications, such as Clinical Decision Support Systems (CDSS). This enables provision of automated support to manage the limitations of guidelines. This thesis introduces a new approach for the problem of combining multiple concurrently implemented CIGs and their interrelations to manage multimorbidity care. MuCIGREF (Multiple Computer-Interpretable Guideline Representation and Execution Framework), is proposed whose specific objectives are to present (1) a novel multiple CIG representation language, MuCRL, where a generic ontology is developed to represent knowledge elements of CPGs and their interrelations, and to create the multimorbidity related associations between them. A systematic literature review is conducted to discover CPG representation requirements and gaps in multimorbidity care management. The ontology is built based on the synthesis of well-known ontology building lifecycle methodologies. Afterwards, the ontology is transformed to a metamodel to support the CIG execution phase; and (2) a novel real-time multiple CIG execution engine, MuCEE, where CIG models are dynamically combined to generate consistent and personalised care plans for multimorbid patients. MuCEE involves three modules as (i) CIG acquisition module, transfers CIGs to the personal care plan based on the patient’s health conditions and to supply CIG version control; (ii) parallel CIG execution module, combines concurrently implemented multiple CIGs by performing concurrency management, time-based synchronisation (e.g., multi-activity merging), modification, and timebased optimisation of clinical activities; and (iii) CIG verification module, checks missing information, and inconsistencies to support CIG execution phases. Rulebased execution algorithms are presented for each module. Afterwards, a set of verification and validation analyses are performed involving real-world multimorbidity cases studies and comparative analyses with existing works. The results show that the proposed framework can combine multiple CIGs and dynamically merge, optimise and modify multiple clinical activities of them involving patient data. This framework can be used to support HCPs in a CDSS setting to generate unified and personalised care recommendations for multimorbid patients while merging multiple guideline actions and eliminating care duplications to maintain their safety and supplying optimised health resource management, which may improve operational and cost efficiency in real world-cases, as well

Betere inzet van klinische richtlijnen in het Prinses Máxima Centrum door standaardisatie en formalisatie in computer interpreteerbare richtlijnen:Innovaties voor de LATER-richtlijn Follow-up kinderkanker: Ontwikkeling van een beslissingsondersteunend systeem

Het Prinses Máxima Centrum voor kinderoncologie is een zorg- en onderzoekscentrum waar zorg en research nauw met elkaar verbonden zijn. Alle kinderen in Nederland met (verdenking op) kinderkanker worden sinds 2018 gediagnosticeerd en behandeld in het Prinses Máxima Centrum. Overlevenden van kinderkanker, zogenaamde survivors, worden na hun behandeling nog blijvend gemonitord en bij late effecten behandeld op de LATER-polikliniek.Dagelijks passen zorgverleners (artsen en verpleegkundigen) klinisch redeneren toe bij het leveren van zorg. Daarbij maken ze gebruik van onder andere klinische richtlijnen en (onderzoeks-)protocollen. Deze zijn veelal beschikbaar als natuurlijk tekst, in PDF-formaat en deels verwerkt in software-systemen. De huidige manier waarop richtlijnen in de zorg beschikbaar zijn en worden ingezet kent problemen. Ze zijn vaak niet eenduidig, hebben geen standaard structuur, zijn achterhaald op het moment dat ze in de praktijk worden toegepast, zijn impliciet, niet computer interpreteerbaar, niet interoperabel en onvoldoende effectief.Het Prinses Máxima Centrum heeft als doel om het gebruik van richtlijnen en protocollen in het Máxima te optimaliseren door deze te standaardiseren en formaliseren in computer interpreteerbare richtlijnen (CIRs). Zo kan informatie in de richtlijnen eenvoudiger gevonden worden en kunnen de regels en adviezen uit de richtlijnen verwerkt worden in een beslissingsondersteunend systeem (BOS). Door de richtlijn en bijbehorende logica op een gestandaardiseerde manier vast te leggen kan deze eenvoudiger gedeeld en geïnterpreteerd worden door andere systemen (eenmalige registratie, meervoudig gebruik) en kennis eenvoudiger/ sneller doorgevoerd worden op het moment dat zorg wordt geleverd.In deze thesis wordt een ontwerp gepresenteerd voor de formalisatie van de LATER-richtlijn naar een CIR op basis van openEHR standaarden en de ontwikkeling van een beslissingsondersteunend systeem (BOS) op de LATER-poli van het Prinses Máxima Centrum.De ontwerpopdracht heeft inzicht gegeven in de te nemen stappen, beschikbare formalisatie-talen en tools om te kunnen komen tot een beslissingsondersteunend systeem voor de LATER-poli. Daarbij is aan de hand van een Proof of Concept (PoC) aangetoond dat het mogelijk is om met internationaal geaccepteerde openEHR standaarden schaalbare, semantisch en syntactisch interoperabele computer interpreteerbaar richtlijnen te ontwikkelen, waarmee adviezen gegenereerd kunnen worden voor de individuele survivor op basis van patiëntdata en klinische richtlijnen.De gekozen programmeertaal (Python) en modulaire opbouw van het beslissingsondersteunend systeem (BOS) maken het mogelijk om de software door te ontwikkelen tot een beslissingsondersteunend systeem dat naast de beslisregels, in de toekomst ook gevoed kan worden met machine learning en artificial intelligence algoritmen ten behoeve van betere beslissingsondersteuning.Bij de gekozen standaarden en ontwikkeling van de software is rekening gehouden met een actief, open (source), goed gedocumenteerd ecosysteem, wat de toekomstbestendigheid van het beslissingsondersteunend systeem ten goede komt. Hierdoor is het aannemelijk dat de onderliggende standaarden en talen voor langere tijd zullen blijven bestaan en het eenvoudiger zal zijn ontwikkelaars en beheerders te vinden die óf al kennis / ervaring hebben óf dit op kunnen doen aan de hand van de beschikbare documentatie.De ontwerpopdracht is succesvol afgerond en heeft waardevolle input geleverd voor het Prinses Máxima Centrum en de LATER use case om verder vervolg te kunnen geven aan de ontwikkeling van een beslissingsondersteunend systeem, waarbij de logica van richtlijnen eenmalig geregistreerd en meervoudig gebruikt kan worden. Met het uitgevoerde onderzoek en ontwerp is een belangrijke bijdrage geleverd aan het vakgebied klinische informatica op het vlak van beslissingsondersteuning

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided

Co-occurrence of common biological and behavioral addictions: using network analysis to identify central addictions and their associations with each other

The present study used network analysis to examine the network properties (network graph, centrality, and edge weights) comprising ten different types of common addictions (alcohol, cigarette smoking, drug, sex, social media, shopping, exercise, gambling, internet gaming, and internet use) controlling for age and gender effects. Participants (N = 968; males = 64.3%) were adults from the general community, with ages ranging from 18 to 64 years (mean = 29.54 years; SD = 9.36 years). All the participants completed well-standardized questionnaires that together covered the ten addictions. The network findings showed different clusters for substance use and behavioral addictions and exercise. In relation to centrality, the highest value was for internet usage, followed by gaming and then gambling addiction. Concerning edge weights, there was a large effect size association between internet gaming and internet usage; a medium effect size association between internet usage and social media and alcohol and drugs; and several small and negligible effect size associations. Also, only 48.88% of potential edges or associations between addictions were significant. Taken together, these findings must be prioritized in theoretical models of addictions and when planning treatment of co-occurring addictions. Relatedly, as this study is the first to use network analysis to explore the properties of co-occurring addictions, the findings can be considered as providing new contributions to our understanding of the co-occurrence of common addictions

The Measure of Adolescent Social Competence (MASC): Development and Initial Validation.

The Measure of Adolescent Social Competence (MASC) was designed to offer a clinically practical way to assess adolescent social function within relevant contexts. The MASC is a 50-item, self-report measure constructed via the following steps: (a) item generation (N = 271 subjects from grades 7, 9, and 11); (b) item selection and development (N = 604 subjects from grades 7, 9, and 11); (c) response enumeration (N = 154 subjects from grades 7, 9, and 11); and (d) response evaluation by adult raters (e.g., parents, teachers, counselors). Initial validation of the MASC examined its relation to peer nominations, teacher ratings of peer acceptance, and a self-rating of conflict with parents. A sample of 598 subjects in grades 6-12 participated. The MASC was found to have adequate internal consistency and test-retest reliability. Greater performance on the MASC was found to be associated with lower levels of parent-adolescent conflict. The relation between MASC scores and measures of peer acceptance were mixed, however. Correlations with peer nominations and teacher ratings were generally nonsignificant. On the other hand, subjects with high (i.e., one sd above the mean) MASC scores earned higher teacher ratings of peer acceptance than subjects with low (i.e., one sd below the mean) scores, and controversial status subjects outperformed all other peer status groups. The development of the MASC and these initial findings are discussed with respect to a proposed tri-component model of adolescent social competence

Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)

Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes. The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question. Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited: • A multidisciplinary approach to the transformation of health and social systems • Success factors for sustainable P5 ecosystems • AI and robotics in transformed health ecosystems • Transformed health ecosystems challenges for security, privacy and trust • Modelling digital health systems • Ethical challenges of personalized digital health • Knowledge representation and management of transformed health ecosystems Table of Contents: 04 Editorial: Managing healthcare transformation towards P5 medicine Bernd Blobel and Dipak Kalra 06 Transformation of Health and Social Care Systems—An Interdisciplinary Approach Toward a Foundational Architecture Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez 26 Transformed Health Ecosystems—Challenges for Security, Privacy, and Trust Pekka Ruotsalainen and Bernd Blobel 36 Success Factors for Scaling Up the Adoption of Digital Therapeutics Towards the Realization of P5 Medicine Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov, Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra 49 EU-Funded Telemedicine Projects – Assessment of, and Lessons Learned From, in the Light of the SARS-CoV-2 Pandemic Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri, Claudia Bighin, Bernd Blobel and Mauro Giacomini 60 A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems Kerstin Denecke and Claude R. Baudoin 73 Modeling digital health systems to foster interoperability Frank Oemig and Bernd Blobel 89 Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence Diego M. López, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin 111 Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel and Stefan Schulz 126 The ethical challenges of personalized digital health Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides and Michael Rigb

The Impact of Video-Based Whole Group Lessons on Place, Manner, and Voicing of Speech Sounds on Reading Achievement

The purpose of this study was to determine if students who received video-based lessons of the place, manner, and voicing (PMV) of speech sounds demonstrated gains on reading achievement compared to students who did not receive the intervention. A quantitative, quasi-experimental study with a pretest, posttest was developed, using the instrumentation of the reading Measures of Academic Progress (MAP). This study addressed gaps in the existing research regarding the impact of video-based instruction of speech sounds on reading achievement. Participants in this study (n=136) were drawn from a convenience sample of kindergarten students attending two elementary schools within the same school district during the 2022-2023 school year. Over the 2022-2023 school year, video lessons on place, manner, and voicing of speech sounds aligned with the district’s reading curriculum were provided to the experimental school’s kindergarten teachers to play for their students. The control school students received standard instruction. Data was collected via a records review following the fall 2022 and spring 2023 reading MAP. Each participant served as their own control, and the analysis of covariance (ANCOVA) was used to analyze the MAP data. The results of the study found a statistically significant difference in overall reading MAP and foundational skills MAP between the control and experimental groups when controlling for prior reading achievement. This study found evidence to support the incorporation of video-based, whole group lessons of PMV on reading achievement. Recommendations for future research include expanding the current study to more schools within the district, region, and state

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

An exploration of executive function, its theoretical construction, and challenges encountered in its understanding and measurement: did neuropsychology get this right?

Section A argued for the importance of cognitive models in providing a theoretical foundation for complex neuropsychological constructs such as ‘executive function’ (EF). It consisted of a narrative review of 29 existing cognitive models of EF, which were reviewed, critiqued, and then integrated into a novel, unified model of EF. This unified account brought together the affective, motivational and attentional processes involved in goal-driven behaviour. Clinical implications were discussed, alongside recommendations for future research in this area. Section B applied a content analysis to systematically examine the ways that EF is described, explained and understood by currently available neuropsychological assessment measures and textbooks, and evaluate these in accordance with current evidence on EF. A total of 29 texts were included. Categories were derived from the current evidence base, including the ‘unified model’ of EF presented in Section A, as well as inductively from the texts. Results suggested that the majority of assessments and textbooks were unlikely to provide such an integrated account, however, there were exceptions. New leads for further theoretical development, and clinical implications were discusse