Willingness to Care-Financial Incentives and Caregiving Decisions.

As population aging will likely lead to an increasing number of people in need of care, the demand for informal care is expected to rise. In this context, it is often discussed whether financial incentives can motivate more individuals to assume caregiving responsibilities. We analyze the potential effect of financial incentives on the provision of informal care by estimating a structural model with endogenous labor supply and caregiving decisions.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Background: Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients.

Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application.

Abstract: Most process mining techniques are primarily automated, meaning that process analysts input information and receive output. As a result, process mining techniques function like black boxes with limited interaction options for analysts, such as simple sliders for filtering infrequent behavior. Recent research tries to break these black boxes by allowing process analysts to provide domain knowledge and guidance to process mining techniques, i.

A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis.

The development of platforms for distributed analytics has been driven by a growing need to comply with various governance-related or legal constraints. Among these platforms, the so-called Personal Health Train (PHT) is one representative that has emerged over the recent years. However, in projects that require data from sites featuring different PHT infrastructures, institutions are facing challenges emerging from the combination of multiple PHT ecosystems, including data governance, regulatory compliance, or the modification of existing workflows.

SALUS-A Study on Self-Tonometry for Glaucoma Patients: Design and Implementation of the Electronic Case File.

Background:  In times of omnipresent digitization and big data, telemedicine and electronic case files (ECFs) are gaining ground for networking between players in the health care sector. In the context of the SALUS study, this approach is applied in practice in the form of electronic platforms to display and process disease-relevant data of glaucoma patients.

Objectives:  The SALUS ECF is designed and implemented to support data acquisition and presentation, monitoring, and outcome control for patients suffering from glaucoma in a clinical setting.

Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture.

Scientific research demands reproducibility and transparency, particularly in data-intensive fields like electrophysiology. Electrophysiology data are typically analyzed using scripts that generate output files, including figures. Handling these results poses several challenges due to the complexity and iterative nature of the analysis process.

Will it run?-A proof of concept for smoke testing decentralized data analytics experiments.

The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution.

Integrated care for older multimorbid heart failure patients: protocol for the ESCAPE randomized trial and cohort study.

Escape: Evaluation of a patient-centred biopsychosocial blended collaborative care pathway for the treatment of multimorbid elderly patients.

Therapeutic Area: Healthcare interventions for the management of older patients with multiple morbidities.

Aims: Multi-morbidity treatment is an increasing challenge for healthcare systems in ageing societies.

Mining context-aware resource profiles in the presence of multitasking.

Healthcare organisations are becoming increasingly aware of the need to improve their care processes and to manage their scarce resources efficiently to secure high-quality care standards. As these processes are knowledge-intensive and heavily depend on human resources, a comprehensive understanding of the complex relationship between processes and resources is indispensable for efficient resource management. Organisational mining, a subfield of Process Mining, reveals insights into how (human) resources organise their work based on analysing process execution data recorded in Health Information Systems (HIS).

An objective, automated and robust scoring using fluorescence optical imaging to evaluate changes in micro-vascularisation indicating early arthritis.

Fluorescence optical imaging technique (FOI) is a well-established and valid method for visualization of changes in micro vascularization at different organ systems. As increased vascularization is an early feature of joint inflammation, FOI is a promising method to assess arthritis of the hands. But usability of the method is limited to the assessors experience as the measurement of FOI is semi-quantitative using an individual grading system such as the fluorescence optical imaging activity score (FOIAS).

Exploring the use of self-sovereign identity for event ticketing systems.

Ticket fraud and ticket scalping activities often cause high costs as well as trust concerns for fans buying event tickets, especially in the secondary ticketing market. To address these issues, several publications and projects have proposed using blockchain technology to enable digital trust and ticket verifiability and thus to improve event ticketing systems. However, these approaches exhibit considerable privacy challenges and fall short concerning reliable, efficient visitor identification, which is necessary for controlling secondary market transactions.

CAREPATH: Developing Digital Integrated Care Solutions for Multimorbid Patients with Dementia.

CAREPATH project is focusing on providing an integrated solution for sustainable care for multimorbid elderly patients with dementia or mild cognitive impairment. The project has a digitally enhanced integrated patient-centered care approach clinical decision and associated intelligent tools with the aim to increase patients' independence, quality of life and intrinsic capacity. In this paper, the conceptual aspects of the CAREPATH project, in terms of technical and clinical requirements and considerations, are presented.

Automated Model Inference for Gaussian Processes: An Overview of State-of-the-Art Methods and Algorithms.

Gaussian process models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable interpolation, regression, and classification. These models are typically instantiated by a Gaussian Process with a zero-mean function and a radial basis covariance function. While these default instantiations yield acceptable analytical quality in terms of model accuracy, GPM inference algorithms automatically search for an application-specific model fitting a particular dataset.

The Mean Single Molecule Rate (mSMR) in the Analysis of Fluorescence Fluctuations: Measurements on DNA Mixtures of Defined Composition.

We present a method for the evaluation of fluorescence fluctuations on the basis of Mandel's Q parameter, using sampling time-dependent factorial cumulants. By relating the Q parameter to the sampling time, we obtain the mean single molecule rate (mSMR), an easy to interpret expression that provides both brightness and diffusion information. The model is suitable for the widely used confocal setups with single photon excitation and a single detection channel.

Data Spine: A Federated Interoperability Enabler for Heterogeneous IoT Platform Ecosystems.

Today, the Internet of Things (IoT) is pervasive and characterized by the rapid growth of IoT platforms across different application domains, enabling a variety of business models and revenue streams. This opens new opportunities for companies to extend their collaborative networks and develop innovative cross-platform and cross-domain applications. However, the heterogeneity of today's platforms is a major roadblock for mass creation of IoT platform ecosystems, pointing at the current absence of technology enablers for an easy and innovative composition of tools/services from the existing platforms.

Efficacy of Short Message Service Text Messaging Interventions for Postoperative Pain Management: Systematic Review.

Background: Addiction to opiates and synthetic opioids poses a major threat to public health worldwide, with pharmaceutical opioids prescribed to manage pain constituting the main problem. To counteract this threat, suitable pain management strategies should be implemented in health care. Monitoring pain management seems to be feasible using telemedicine with a certain degree of resource intensity and digitization.

The PICASO cloud platform for improved holistic care in rheumatoid arthritis treatment-experiences of patients and clinicians.

Background: Multimorbidity raises the number of essential information needed for delivery of high-quality care in patients with chronic diseases like rheumatoid arthritis (RA). We evaluated an innovative ICT platform for integrated care which orchestrates data from various health care providers to optimize care management processes.

Methods: The Horizon2020-funded research project PICASO (picaso-project.

Distributed Skin Lesion Analysis Across Decentralised Data Sources.

Skin cancer has become the most common cancer type. Research has applied image processing and analysis tools to support and improve the diagnose process. Conventional procedures usually centralise data from various data sources to a single location and execute the analysis tasks on central servers.

Software-based planning of ultrasound and CT-guided percutaneous radiofrequency ablation in hepatic tumors.

Objectives: Radiofrequency ablation (RFA) can be associated with local recurrences in the treatment of liver tumors. Data obtained at our center for an earlier multinational multicenter trial regarding an in-house developed simulation software were re-evaluated in order to analyze whether the software was able to predict local recurrences.

Methods: Twenty-seven RFA ablations for either primary or secondary hepatic tumors were included.

Innovative Imaging Technique for Visualization of Vascularization and Established Methods for Detection of Musculoskeletal Inflammation in Psoriasis Patients.

Psoriasis (PsO) is one of the common chronic inflammatory skin diseases. Approximately 3% of the European Caucasian population is affected. Psoriatic arthritis (PsA) is a chronic immune-mediated disease associated with PsO characterized by distinct musculoskeletal inflammation.

Deep learning-based clustering approaches for bioinformatics.

Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.

Clinical evaluation of in silico planning and real-time simulation of hepatic radiofrequency ablation (ClinicIMPPACT Trial).

Objectives: To evaluate the accuracy and clinical integrability of a comprehensive simulation tool to plan and predict radiofrequency ablation (RFA) zones in liver tumors.

Methods: Forty-five patients with 51 malignant hepatic lesions of different origins were included in a prospective multicenter trial. Prior to CT-guided RFA, all patients underwent multiphase CT which included acquisitions for the assessment of liver perfusion.

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT).

Introduction: Radio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available.

Objectives: ClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software.