Sessions

This presentation is about our work on a brain tumor detection using clustering methods. The ultimate goal is to use this process in a Digital Twin prototype

The smart data extractor is a semi-automatic data extraction software to assist clinician in the completion of case report forms.It is a pipeline build on top of Dr Warehouse, the Necker (ap-hp, France) data warehouse.It is based on thesauri and regular expression.It saves time and error and it is clinician friendly and easy to use.

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

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

Patient accessible electronic health records (PAEHRs) have been proposed as a means to improve patient safety and documentation quality, as patients become an additional source to detect mistakes in the records. In pediatric care, healthcare professionals (HCP) have noted a benefit of parent proxy users correcting errors in their child’s records. However, the potential of adolescents has so far been overlooked, despite reports of reading records to ensure accuracy. The present study examines errors and omissions identified by adolescents, and whether patients reported following up with HCPs. Survey data was collected during three weeks in January and February 2022 via the Swedish national PAEHR. Of 218 adolescent respondents, 60 reported having found an error (27.5%) and 44 (20.2%) had found missing information. Most adolescents did not take any action upon identifying an error or an omission (64.0%). Omissions were more often perceived as serious than errors. These findings call for development of policy and PAEHR design that facilitates reports of errors and omissions for adolescents, which could both improve trust and support the individual’s transition into an involved and engaged adult patient.

In this presentation I talk about a setup for using remote eye-tracking on a touchscreen tablet to evaluate user interaction for older adults interacting with a user-driven hearing test. By using video recordings to support the eye-tracking data, it was possible to evaluate quantitative usability metrics that could be compared to other research findings. The video recordings revealed useful information to distinguish between reasons for gaps in data and missing data and to inform future similar studies of human-computer interaction on a touch screen. Using only portable equipment allows researchers to move to the location of the user and investigate the user interaction of devices in real-world scenarios

The aim of this work is to develop and evaluate a multi-stage procedure model for the identification of use problems and optimization of usability using biosignal data. The concept is divided into 5 steps: 1. static analysis of data to identify use problems; 2. conducting interviews within the context of use and requirements analysis to investigate problems in more detail; 3. developing new interface concepts to implement the requirements and a prototype of an interface including dynamic visualization of data; 4. formative evaluation using an unmoderated remote usability test; 5. usability test with realistic scenarios and influencing factors in the simulation room. The concept was evaluated in the ventilation setting as an example. The procedure allowed the identification of use problems in the ventilation of patients as well as the development of suitable concepts and their evaluation to counteract use problems. To relieve users, ongoing analyses of biosignals with respect to the use problem are to be carried out. To overcome technical barriers, further development is needed in this area.

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