Sessions

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

The integration of design thinking into medical informatics education is essential for development of innovative and effective health information technologies in our work domain. Design thinking is a problem-solving approach which focuses on understanding complex user needs and creating innovative solutions befitting those complex needs. It is a widely used approach for developing user-centered products and services, and distils creative methodologies to improve usability and effectiveness of health information systems.
This workshop discusses use cases for design thinking in medical informatics education, provides an overview of the lessons learned of incorporating design thinking into the curriculum, and will challenge the audience to experiment with two of the design thinking phases. The workshop also outlines the implications of design thinking for medical informatics educators, students and our work field, and provides suggestions for incorporating design thinking into existing medical informatics programs.

In order to build the best query to guide the literature search on clinical decision support systems applied to nursing clinical practice, we developed an iterative process capitalizing on previous systematic reviews published on similar topics

Questions or else: richard.noll@kgu.de

Autism spectrum disorder (ASD) is a developmental disability caused by differences in the brain regions. Analysis of differential expression (DE) of transcriptomic data allows for genome-wide analysis of gene expression changes related to ASD. De-novo mutations may play a vital role in ASD, but the list of genes involved is still far from complete. Differentially expressed genes (DEGs) are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we employed a machine learning-based approach to identify the differential gene expression between ASD and Typical Development (TD). The gene expression data of 15 ASD and 15 TD were obtained from NCBI GEO database. Initially, we extracted the data and used a standard pipeline to preprocess the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We identified the top 10 prominent differential genes and compared them with the statistical test results. Our results show that the proposed RF model yields 5-fold cross-validation accuracy, sensitivity and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Moreover, we found 34 unique DEG chromosomal locations having influential contributions in identifying ASD from TD. We have also identified chr3:113322718-113322659 as the most significant contributing chromosomal location in discriminating ASD and TD. Our machine learning-based method of refining DE analysis is promising for finding biomarkers from gene expression profiles and prioritizing DEGs. Moreover, our study reported top-10 gene signatures for ASD may facilitate the development of reliable diagnosis and prognosis biomarkers for screening ASD.Keywords: Gene expression data, NCBI, Autism Spectrum Disorder, Random Forest, Statistical test

The German DiGA process has raised international interest, with more than 30 digital solutions which has received temporary or permanent reimbursement. In this session there will be an overview of the process and summary of how the insurance companies see the process. What can we learn from their experience?