Programpunkter

Separate registration required: https://www.mie2023.org/tutorials, The open-source Python framework AUCMEDI offers a solution to the describedchallenges. The software package not only offers a library as a 'high-level' API for thestandardized construction of modern medical image classification pipelines, but alsoreproducible installation and direct application via Dockerization and automatichyperparameter detection. With AUCMEDI, researchers are able to set up a completeas well as easy-to-integrate medical image classification pipeline with just a few linesof code. AUCMEDI is available as a Python package via PyPI ('pip install aucmedi')and as a repository via GitHub with detailed documentation, examples, and bindings tomodern DevOps (CI/CD) techniques: https://frankkramer-lab.github.io/aucmedi/.

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

Adverse Drug Reactions (ADRs) cause a significant impact on patients’ Quality of Life (QoL) and vastly increase costs, especially regarding chronic diseases. To this end, we propose a platform that aims at supporting the management of patients with Chronic Lymphocytic Leukemia (CLL), via an eHealth platform facilitating inter-physician interaction and the provision of treatment consultation by a specialized ADR management team comprised of CLL experts.

The MIE conference has always been a hub for innovation and cutting-edge technology. This year was no exception several demonstrators showcase their unique solutions to healthcare problems. Use the opportunity to take a closer look at some of the most striking demonstrators present at the conference, highlighting their diversity and the potential impact they could have on various healthcare-related challenges

A demonstration of an open source software prototype for improving infection control in hospitals called Smart Infection Control System (SmICS). It provides services for tracing contacts between patients, showing in-depth details for microbiological findings, patients movements and wards and offers epidemiological curves without having a vendor-dependent data storage within the application, because it is fully based upon an open interoperability standard (openEHR).

Short introduction to the National Research Data Infrastructure for Personal Health Data (NFDI4Health) and proposed solutions towards findability and interoperability of clinical and epidemiological studies. Two services will be presented: the German Central Health Study Hub to support the findability of studies and the Metadata Annotation Workbench to support enrichment of data collection instruments with standardized vocabularies on the study item level.