Recent research has indicated an increase in the likelihood and impact of tree failure. The potential for trees to fail relates to various biomechanical and physical factors. Strikingly, there seems to be an absence of tree risk assessment methods supported by observations, despite an increasing availability of variables and parameters measured by scientists, arborists and practitioners. Current urban tree risk assessments vary due to differences in experience, training, and personal opinions of assessors. This stresses the need for a more objective method to assess the hazardousness of urban trees. The aim of this study is to provide an overview of factors that influence tree failure including stem failure, root failure and branch failure. A systematic literature review according to the PRISMA guidelines has been performed in databases, supported by backward referencing: 161 articles were reviewed revealing 142 different factors which influenced tree failure. A meta-analysis of effect sizes and p-values was executed on those factors which were associated directly with any type of tree failure. Bayes Factor was calculated to assess the likelihood that the selected factors appear in case of tree failure. Publication bias was analysed visually by funnel plots and results by regression tests. The results provide evidence that the factors Height and Stem weight positively relate to stem failure, followed by Age, DBH, DBH squared times H, and Cubed DBH (DBH3) and Tree weight. Stem weight and Tree weight were found to relate positively to root failure. For branch failure no relating factors were found. We recommend that arborists collect further data on these factors. From this review it can further be concluded that there is no commonly shared understanding, model or function available that considers all factors which can explain the different types of tree failure. This complicates risk estimations that include the failure potential of urban trees.
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Risk assessments on trees in urban areas and roadside plantings have become common practice and a large body of information exists on qualitative aspects on the risks of tree failure. Quantitative analysis of financial damage due to tree failure is generally lacking. The objective of this paper is to determine the amount of tree failure related property damage and to derive possible trends in the number of cases and monetary claims and compensations. This paper presents the analysis of 1610 observations on urban tree failure in the Netherlands. The data originate from two different sources, i.e. jurisprudence (4% of the data) and 21 municipalities (96%). The data covers property damage in urban areas between the early sixties and 2010. Within municipalities, paid compensations due to tree failure are found to range from €0 to € 49,296 with an average of €2,244 per paid compensation. Results demonstrate a significant annual increase in tree failure as well as in paid compensations.
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The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the nonphysical origins of HF.
DOCUMENT
Een zestal lectoren richt het Platform Inzet van Technologie voor Gezondheid en Welzijn op. Het overkoepelende doel van dit platform is doorontwikkeling, adoptatie en duurzame implementatie van reeds ontwikkelde technologie voor (gezondheids)zorg en welzijn. De zorgconsument wordt gezien als belangrijkste gebruiker van eHealth-technologie. Hun wensen staan daarom voorop. Daarnaast spelen huidige en toekomstige zorgprofessionals een belangrijke rol in het behalen van het overkoepelende doel evenals (private) ontwikkelaars van eHealth. Het platform heeft subdoelen geformuleerd op drie gebieden, te weten kennis & onderzoek, onderwijs & praktijk en visievorming & zichtbaarheid. Deze zijn eveneens gericht op de zorgconsument (bijvoorbeeld als het gaat om het verhogen van eigen regie en zelfmanagement), gericht op zorgprofessionals (bijvoorbeeld als het gaat om kwaliteit, doelmatigheid en continuïteit van zorg) en op andere stakeholders zoals bedrijven die technologieën ontwikkelen en willen opschalen. Het platform kent een open karakter. Dit uit zich onder meer in het feit dat uitbreiding van het platform vast agendapunt van de stuurgroep van het platform is, in het actief uitnodigen van relevante stakeholders op diverse bijeenkomsten (zoals inspiratiesessies en symposia) en in het vrij beschikbaar stellen van producten van het platform (zoals een publicatie met best practices and good failures en diverse 'white papers'). Het platform legt nadruk op het delen van kennis over technologie voor gezondheid en welzijn en op afstemming van onderzoekagenda's en ontwikkelingen met collega-platforms en -netwerken. Daarnaast ligt de nadruk op co-creatie met gebruikers van de technologie én op samenwerking met een diversiteit aan partners, waaronder, naast de genoemde zorgconsumenten en hun vertegenwoordigers, kennisinstellingen en private partners. Het eerste jaar wordt gezien als opbouwfase. Hierin wordt het netwerk versterkt, de visie en de plannen aangescherpt en gestart met financiering zoeken voor concrete activiteiten. In de loop van de tijd verschuift de nadruk naar samenwerking in (onderzoeks)projecten. Hiermee zorgt het platform niet alleen voor financiële middelen maar ook voor draagkracht en verbinding tussen partijen voor toekomstige samenwerking.
