Expectation of clinical decision support systems: a survey study among nephrologist end-users.

Background: Chronic kidney disease (CKD), a major public health problem with differing disease etiologies, leads to complications, comorbidities, polypharmacy, and mortality. Monitoring disease progression and personalized treatment efforts are crucial for long-term patient outcomes. Physicians need to integrate different data levels, e.

Fake paper identification in the pool of withdrawn and rejected manuscripts submitted to Naunyn-Schmiedeberg's Archives of Pharmacology.

Honesty of publications is fundamental in science. Unfortunately, science has an increasing fake paper problem with multiple cases having surfaced in recent years, even in renowned journals. There are companies, the so-called paper mills, which professionally fake research data and papers.

Alternative splicing impacts microRNA regulation within coding regions.

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation.

Towards a Recommendation for Good Health Data Modeling (GHDM) - Results of Expert Interviews.

Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling.

Anomaly detection in mixed high-dimensional molecular data.

Motivation: Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies.

The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials.

Aims: Type 2 diabetes (T2D) is rising worldwide. Self-care prevents diabetic complications. Lack of knowledge is one reason patients fail at self-care.

Proteomics reveals a global phenotypic shift of NK cells in HCV patients treated with direct-acting antivirals.

Chronic hepatitis C virus (HCV) infections compromise natural killer (NK)-cell immunity. Direct-acting antivirals (DAA) effectively eliminate HCV, but the long-term effects on NK cells in cured patients are debated. We conducted a proteomic study on CD56 NK cells of chronic HCV-infected patients before and 1 year after DAA therapy.

Eos and OMOCL: Towards a seamless integration of openEHR records into the OMOP Common Data Model.

Background: The reuse of data from electronic health records (EHRs) for research purposes promises to improve the data foundation for clinical trials and may even support to enable them. Nevertheless, EHRs are characterized by both, heterogeneous structure and semantics. To standardize this data for research, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standard has recently seen an increase in use.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

Background: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data.

Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons.

Background: Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial.

Reflections Towards the Future of Medical Informatics.

Objective: To provide guidance on the future development and role of medical informatics, or biomedical and health informatics, in form of reflections.

Method: To report on the author's previous activities as a medical informatician, which spans almost half a century. It began in 1973 when he started to study medical informatics.

An Automated System to Distribute Students to Clerkships.

Participating in clerkships with general practitioners (GPs) is integral to studying medicine. The students gain deep and valuable insights into the everyday working practice of GPs. The central challenge is organizing these clerkships to distribute the students to the participating doctors' offices.

Achieving Interoperable Datasets in Pediatrics: A Data Integration Approach.

Despite their increased secondary value for developing applications and knowledge gain, routine, harmonized and standardized datasets are often not available in Pediatrics. We propose a data integration pipeline towards an interoperable routine dataset in pediatric intensive care medicine. Our three-level approach involves identifying relevant data from primary source systems, developing local data integration processes, and converting data into a standardized, interoperable format using openEHR.

Harmonisation of German Health Care Data Using the OMOP Common Data Model - A Practice Report.

Data harmonization is an important step in large-scale data analysis and for generating evidence on real world data in healthcare. With the OMOP common data model, a relevant instrument for data harmonization is available that is being promoted by different networks and communities. At the Hannover Medical School (MHH) in Germany, an Enterprise Clinical Research Data Warehouse (ECRDW) is established and harmonization of that data source is the focus of this work.

Do Health Apps Stigmatize People with Obesity?

Apps in the "Medicine" category of Apple's App Store were examined concerning the potential stigmatization of people with obesity through word and image language. Only 5/71 potentially stigmatizing apps related to obesity were identified. Stigmatization in this context can occur, for example, through the excessive promotion of very slim people in connection with weight loss-related apps.

Conciliation and Approval of Quality Principles for the Self-Declaration of the Quality of Health Apps.

This poster describes the conciliation and approval process of the unified set of criteria for self-declaration of health app quality. The timeline underlines the necessity of transparency and open communication in regulations.

COVID-19 Mobile Apps Trends Derived from Long-Term Google Play Analysis.

In this paper, we describe the 5-year trends of COVID-related mobile apps in the Google Play platform obtained by retrospectively analyzing app descriptions. Out of 21764 and 48750 unique apps available free of charge in the "medical" and "health and fitness", there were 161 and 143 COVID-related apps, respectively. The prominentrise in apps' prevalence occurred in January 2021.

Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias.

Background: Smartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms.

Objectives: The aim of the study was to establish a novel, expanded approach of a semiautomated retrospective App Store analysis (SARASA) to identify and characterise health apps in the context of cardiac arrhythmias.

The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements.

Research conducted on mobile apps providing mental health services has concluded that patients with mental disorders tend to use such apps to maintain mental health balance technology may help manage and monitor issues like bipolar disorder (BP). This study was conducted in four steps to identify the features of designing a mobile application for BP-affected patients including (1) a literature search, (2) analyzing existing mobile apps to examine their efficiency, (3) interviewing patients affected with BP to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Literature search and mobile app analysis resulted in 45 features, which were later reduced to 30 after the experts were surveyed about the project.

Developing a mobile health application for wound telemonitoring: a pilot study on abdominal surgeries post-discharge care.

Background: Many early signs of Surgical Site Infection (SSI) developed during the first thirty days after discharge remain inadequately recognized by patients. Hence, it is important to use interactive technologies for patient support in these times. It helps to diminish unnecessary exposure and in-person outpatient visits.