Programpunkter

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

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.

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.

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/.

We benchmark the influence of four types of noise annotated in the public dataset PTB-XL on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead ECGs. For this we use the noise metadata provided by human experts as well as a quantitative signal-to-noise ratio for assigning a signal quality to each ECG and analyze the respective DL accuracy. We conclude that the issue of processing noisy electrocardiography data can be addressed successfully by Deep Learning methods.

Forsante Anticoagulation is a CE certified digital service that automates the dosing of anticoagulants and treatment follow-up. Automation capabilities save up to 80% of clinicians’ time spent in the anticoagulation treatment process. Treatment quality is improved by standardising treatment models. Our data shows that patients’ treatment balance is enhanced over time in terms of TTR (time in therapeutic range). Better TTR reduces adverse events and improves safety.