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In this paper, we present an approach to improve the accuracy of hand tremor severity in Parkinson's patients in real-life unconstrained environments. The system leverages data achieved from daily interaction people with their smartphones and uses technologies for classifying and combining data. We describe the basic concept of data fusion and demonstrate how different combination techniques can improve the accuracy of tremor detection. The fusion enable to achieve the 23.5% improvement with respect to the average of individual classification models.It is necessary for hospitals to be able to compare the usability of electronic health records (EHR) before acquisition. Adding usability as a critical element of the procurement process is therefore crucial. During the competitive usability evaluation of several EHRs, the usability walkthrough method has the potential of making end-users more active in the procurement process than demonstrations. This case study presents first results of a comparison of three EHRs performed by nine representative end-users. All users uncovered usability problems while performing their scenarios. Selleck U0126 The results show that none of the EHRs evaluated is without major usability problems. These problems have been well-known to human factors researchers for a long time.The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes adherence, persistence of use, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting (yes/no), and opting-in to receive injection reminders (yes/no), were used as a proxy for engagement. Patients were assigned to the low-engagement group (n=1,752) when all of their features had the low-engagement flag (>2h, no, no) and to the high-engagement group (n=1,081) when all of their features had the high-engagement flag (≤2h, yes, yes). The low-engagement group was down-sampled to 1,081 patients (subsample of n=37 for growth) using the iterative proportional fitting algorithm. Statistical tests were used to study the impact of engagement to the outcomes. The results show that all three outcomes were significantly improved by a factor varying from 1.8 up to 2.2 when the engagement level was high. These results should encourage the promotion of engagement and associated behaviors by both patients and healthcare professionals.In the current COVID-19 pandemic, the importance of digital media as a source of information for health-related behavior is impressively demonstrated. Until now there has been a lack of national research on the influence of socioeconomic differences in digital literacy and in the use of COVID-19 information. This study aims to analyze the influence of educational status and subjective social status on digital literacy and on the ability in using COVID-19 information. Data from a cross-sectional online survey were used. The results indicate social differences in digital literacy and in the ability to critically evaluate COVID-19 information.An online advanced training for professionals within the healthcare sector was developed including a problem statement to be solved following the steps of problem-based learning (PBL).The findings show that it is feasible to transfer PBL electronically (ePBL) where participants favoured the flexibility and time independency of ePBL. However, the evaluation revealed issues with the learning platform, insufficient technical conditions in the hospitals and a lack of personal exchange. Thus, ePBL offers advantages especially for advanced training in the healthcare sector but requires adaptations and the necessary technical prerequisites.Due to the fast-spreading of COVID-19 during the pandemic, decision-makers turned into innovative digital solutions for data collection in order to make well-informed public health decisions based on reliable data from verified sources. This work describes one of such solutions, implemented in partnership with the Ministry of Health in Argentina.Data-driven methods in biomedical research can help to obtain new insights into the development, progression and therapy of diseases. Clinical and translational data warehouses such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART are important solutions for this. From the well-known FAIR data principles, which are used to address the aspects of findability, accessibility, interoperability and reusability. In this paper, we focus on findability. For this purpose, we describe a portal solution that acts as a catalogue for a wide range of data warehouse instances, featuring a central access point and links to training material, such as user manuals and video tutorials. Moreover, the portal provides an overview of the status of multiple warehouses for developers and a set of statistics about the data currently loaded. Due to its modular design and the use of modern web technologies, the portal is easy to extend and customize to reflect different corporate designs and institutional requirements.Disease trajectories model patterns of disease over time and can be mined by extracting diagnosis codes from electronic health records (EHR). Process mining provides a mature set of methods and tools that has been used to mine care pathways using event data from EHRs and could be applied to disease trajectories. This paper presents a literature review on process mining related to mining disease trajectories using EHRs. Our review identified 156 papers of potential interest but only four papers which directly applied process mining to disease trajectory modelling. These four papers are presented in detail covering data source, size, selection criteria, selections of the process mining algorithms, trajectory definition strategies, model visualisations, and the methods of evaluation. The literature review lays the foundations for further research leveraging the established benefits of process mining for the emerging data mining of disease trajectories.
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