In recent decades, the number of cases of knee arthroplasty among people of working age has increased. The integrated clinical pathway ‘back at work after surgery’ is an initiative to reduce the possible cost of sick leave. The evaluation of this pathway, like many clinical studies, faces the challenge of small data sets with a relatively high number of features. In this study, we investigate the possibility of identifying features that are important in determining the duration of rehabilitation, expressed in the return-to-work period, by using feature selection tools. Several models are used to classify the patient’s data into two classes, and the results are evaluated based on the accuracy and the quality of the ordering of the features, for which we introduce a ranking score. A selection of estimators are used in an optimization step, reorganizing the feature ranking. The results show that for some models, the proposed optimization results in a better ordering of the features. The ordering of the features is evaluated visually and identified by the ranking score. Furthermore, for all models, higher accuracy, with a maximum of 91%, is achieved by applying the optimization process. The features that are identified as relevant for the duration of the return-to-work period are discussed and provide input for further research.
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Gedeelde besluitvorming is in de praktijk niet zo eenvoudig. SDM vraagt van zowel de verpleegkundige als de patiënt eigenschappen die niet vanzelfsprekend aanwezig zijn. De verpleegkundige dient in staat te zijn verschillende mogelijkheden met de voor- en nadelen te presenteren en daarnaast de patiënt de ruimte te geven een keuze te maken die het best bij hem past. Deze werkwijze past goed in een persoonsgerichte visie, waarin gedeelde besluitvorming of samen beslissen en empowerment belangrijke elementen zijn.
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Comparisons of visual perception, response-selection, and response-execution performance were made between Type 2 diabetes mellitus patients and a matched nondiabetic control group. 10 well-controlled male patients with Type 2 diabetes without diabetic complications (M age 58 yr.) and an age and IQ-matched non-diabetic control group consisting of 13 male healthy volunteers (M age 57 yr.) were included. Significant differences were found only between the two groups on response-selection performance, which concerns the selection and preparation of an appropriate motor action.
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Inhalation therapy is essential for the management of respiratory conditions such as asthma and chronic obstructive pulmonary disease. However, current inhalation systems face limitations, including polydisperse aerosols that reduce drug delivery efficiency and complex treatment regimens that affect patient adherence. To improve drug targeting and efficacy, Gilbert Innovation B.V. is developing a next-generation soft-mist inhaler based on electrohydrodynamic atomization (EHDA), which produces uniform micrometer sized droplets. Effective drug delivery requires high flow rates and precise aerosol discharge to ensure deep lung deposition while minimizing losses to the device and oropharynx. To achieve this, the device employs a multi-nozzle system for increased flow and corona discharge needles for charge neutralization. However, ensuring uniform neutralization across multiple nozzles and maintaining stable electrospray operation remain key challenges. COSMIC aims to increase system robustness by optimizing neutralization efficiency, refining material selection, and controlling electrospray stability under varying conditions. The electrospray control system will incorporate advanced strategies leveraging computer vision, machine learning and big data analytics. These innovations will increase efficiency, accessibility and patient comfort in inhalation therapy.
For English see below In dit project werkt het Lectoraat ICT-innovaties in de Zorg van hogeschool Windesheim samen met zorganisaties de ZorgZaak, De Stouwe, en IJsselheem en daarnaast Zorgcampus Noorderboog, Zorgtrainingscentrum Regio Zwolle, Patiëntenfederatie NPCF, VitaalThuis, ActiZ, Vilans, V&VN, Universiteit Twente en het Lectoraat Innoveren in de Ouderenzorg van Windesheim aan het in staat stellen van wijkverpleegkundigen om autonoom en doelmatig, op basis van klinisch redeneren, eHealth te indiceren en in te zetten bij cliënten. De aanleiding voor dit project wordt gevormd door de wijzigingen per 1 januari 2015 in de Zorgverzekeringswet. Wijkverpleegkundigen zijn sindsdien zelf verantwoordelijk voor de indicatiestelling en zorgtoewijzing voor verzorging en verpleging thuis: zij moeten bepalen welke zorg hun cliënten nodig hebben gezien hun individuele situaties, en hoe die zorg het best geleverd kan worden. Zorgverzekeraars leggen hierbij minimumeisen op, o.a. met betrekking tot de inzet van eHealth. Wijkverpleegkundigen hebben op dit moment echter niet of nauwelijks ervaring met het inzetten en toepassen van technologische toepassingen zoals eHealth. Vraagarticulatie leidde tot de volgende praktijkvraagstelling: 1. Hoe kunnen wijkverpleegkundigen worden voorzien in hun informatiebehoefte over eHealth? 2. Hoe kunnen wijkverpleegkundigen worden ondersteund in hun klinisch redeneren over het inzetten van eHealth bij hun cliënten? 3. Hoe kunnen wijkverpleegkundigen worden ondersteund bij het inzetten van eHealth in hun zorgproces? Het project levert hiertoe drie bijdragen: - De eerste bijdrage is een duurzaam geborgde keuzehulp (een app voor tablet of smartphone) waarmee wijkverpleegkundigen toegang hebben tot de benodigde informatie over eHealth-toepassingen en die aansluit bij de manier waarop wijkverpleegkundigen zorg indiceren (bijvoorbeeld door relaties te leggen tussen NIC-interventies en bijpassende eHealth-toepassingen). - Informatievoorziening is niet een afdoende antwoord op de handelingsverlegenheid van de wijkverpleegkundige omdat eHealth sterk in ontwikkeling is en blijft waardoor er altijd een discrepantie zal bestaan tussen de beschikbare en de benodigde informatie. . De tweede bijdrage van dit project is daarom kennis over (en inzicht in) het klinisch redeneren over de inzet van eHealth. Deze kennis wordt in het project doorvertaald naar een trainingsmodule die erop is gericht om het klinisch redeneren van wijkverpleegkundigen over het inzetten van eHealth en andere thuiszorgtechnologie bij hun cliënten te versterken. - De derde bijdrage van dit project omhelst inbedding van bovengenoemde resultaten in het verpleegkunde-onderwijs van onder meer Windesheim en in nascholingstrajecten voor wijkverpleegkundigen. Voor duurzame, bredere inbedding in het onderwijs wordt samengewerkt met regionale zorgonderwijsnetwerken. In this project the research group IT-innovations in Health Care of Windesheim University of Applied Sciences cooperates with care organisations de ZorgZaak, De Stouwe, and IJsselheem, and stakeholders Zorgcampus Noorderboog, Zorgtrainingscentrum Regio Zwolle, Patiëntenfederatie NPCF, VitaalThuis, ActiZ, Vilans, V&VN, University of Twente, and research group Innovation of Care of Older Adults of Windesheim to enable home care nurses to autonomously and adequately, based on clinical reasoning, allocate eHealth and implement it in patient care. The motivation behind this project lies in the alterations in the care insurance legislation per January 2015. Since then, home care nurses are responsible for the care allocation of all care at home: they determine which care their clients require, taking into account the individual situations, and how this care can best be delivered. Care insurance companies impose minimum requirements for this allocation of home care, among others concerning the implementation of eHealth. Home care nurses, however, have no or limited information about and experience with technical applications like eHealth. Articulation of the demands of home care nurses resulted in the following questions: 1. How can home care nurses be provided with information concerning eHealth? 2. How can home care nurses be supported in their clinical reasoning about the deployment of eHealth by their patients? 3. How can home care nurses be supported when deploying eHealth in their care process? This project contributes in three ways: " The first contribution is a sustainable selection tool (an app for tablet or smartphone) to be used by home care nurses to provide them with the required information about eHealth applications. This selection tool will work in accordance with how home care nurses allocate care, e.g. by relating NIC-interventions to matching eHealth applications. " Providing information is an insufficient, although necessary, answer to the demands of home care nurses because of continuously developing eHealth applications. Hence, the second contribution of this project is knowledge about (and insight in) the clinical reasoning about the deployment of eHealth. This knowledge will be converted into a training module aimed at strengthening the clinical reasoning about the deployment of eHealth by their patients. " The third contribution of this project concerns embedding the selection tool and the training module in regular education (among others at Windesheim) and in refresher courses for home care nurses. Cooperation with regional care education networks will ensure sustainable and broad embedding of both the selection tool and the training module.
How does a specific lung cancer become resistant towards medication.The occurrence of a chromosomal translocation resulting in a ROS1 gene fusion in lung cancer is relatively rare with around 1-2% of all cases. Both Dutch (Stichting Merels Wereld) and world-wide (ROS1ders) patient advocacy groups work hard to raise awareness and bring researchers together to close the knowledge gap on ROS1 driven oncogenesis and increase the optionsfor treatment. A notorious hurdle is to achieve durable responses due to development of resistance.Ongoing mutations occurring in tumour cells lead to a heterogeneous genomic landscape and will result in outgrowth of the fastest growing tumour cell population resistant to the applied drug. The currently known resistance mechanisms can be divided in on-target (i.e. mutations in the kinasedomain of ROS1) and off-target (providing ROS1 independent growth support) mechanisms. The currently available drugs target the ROS1-fusion gene positive lung cancer cells. In addition, some of the drugs also target cancer cells with specific ROS1 resistance mutations allowing effective sequentialtreatment upon disease progression. Selection of the most optimal treatment is largely a ‘trial and error’ approach. Patients and their doctors ask for better prediction of the most effective follow-up treatment upon development of resistance. Medical Life Science & Diagnostics can help to improvetreatment by developing cell culture models which mimic the situation in resistant tumour cells.Understanding the impact of specific mutations on disease behaviour will aid in the development of patient-tailored therapeutic approaches, ultimately improving patient outcomes.