Sessions

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

Presentation of the Regional digital health action plan for the WHO European Region 2023–2030.

Artificial intelligence (AI) has been used to support decision making in the mental health domain for both healthcare professionals and patients on different levels in the healthcare systems and self-care services. AI has been included in several health applications such as predicting mental health problems, diagnostics, treatment plans, workflows, and other duties that require decision support. Understanding the use of AI tools and how it is applied for support decision making in the mental health would contribute in improving the design, development and implementation in practice. Further knowledge will also help benefit shaping person-centered care approaches when using AI tools by understanding the dynamics used nowadays in support decision making and how to include patients in the development of future processes and tools,It is unclear what types of decisions that AI tools most commonly participate in supporting in mental health, how these tools are applied, and whether its implementation is engaging patients in the process of decision making or not, this uncertainty may contribute in shifting the decision- making process towards more technology polarization and far from patient centeredness. Therefore, a scoping review is conducted to map the characteristics of research done on AI tools for supporting decision making in the mental health field to contribute to the body of knowledge regarding how decisions are supported. In the proposed lecture we will present our findings regarding the use of AI in mental health and reflections on support decision making.

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.

A great pitch@vitalisAt A great pitch@vitalis, the most innovative Healthtech companies will showcase new digital solutions in healthcare. Attendees can listen to short pitches where approximately 10 companies present their innovations, followed by questions from a distinguished panel. The participating companies will be announced in early May. We welcome you to join us for this engaging event with high energy and excitement!When: Tuesday, May 23, from 11 am to 12:30 pm
Where: Vitalis Matchmaking and Event Area (in the exhibition)
 
A conference pass is not required as it is included in the exhibition ticket.

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

Within Nordic cooperation and with funding from the Nordic Council of Ministers, a number of projects are underway as part of the Nordic Vision 2030 goals; The Nordic region will become the most sustainable and integrated region in the world. Some examples of ongoing and completed projects are presented during this session. Integrated Healthcare and care (iHAC) 2021-2024 is run by the Centre for Rural Medicine in Storuman Sweden and the Nordic Welfare centre. The project focuses on the citizen perspective in national and regional models for integrated management of healthcare and social care including the data management. Nordforsk runs the more research-oriented project Nordic Commons with a focus on secondary use of health data for research. Nordic Innovation has additional investments related to health data promotion from an innovation perspective.THL in Finland runs the Finnish presidency project 2021-2023, World's Smoothest Cross-Border Mobility and Daily Life Through Digitalisation with a focus on e-prescriptions and Patient Summary. In 2021, the project published the report Baseline study of cross-border data exchange in the Nordic and Baltic countries. The project is partly a continuation of the project the Swedish E-health authority carried out in the Nordic project Care and care at a distance (VOPD) 2018-2021 with a focus on cross border e-prescriptions and patient summaries.This session can be seen as a brief introduction related to what happens within the Nordic Cooperation related to health data.

Nursing practice is transforming at pace to lead improvements in patient outcomes in hospital, primary and community practice. New opportunities are emerging in advanced nursing practice which is a new level of practice defined by knowledge and skills acquisition. There are 4 leadership domains of advanced clinical practice: clinical, professional, health system and health policy leadership. A key component of health systems leadership is driving system change through digital transformation and clinical informatics. The approach to developing advanced clinical practice in east London will be described. Embedded in our approach is the systematic use of our multidisciplinary ‘WeConnect’ digital transformation programme. To implement change and training, a thorough clinical and technical understanding are both needed. This is where the role of the nursing and medical informaticists alongside innovative and engaging advanced clinical practitioners is essential. Key skills for advanced clinical practitioners are the development of autonomous clinical practice based on a foundation of digital skills. These include: use of SNOMED-CT for problem and procedure listing and decision support, analytics and risk based assessments derived from the real world data recorded in the electronic health record. Supporting the journey towards higher levels of digital and SNOMED-CT maturity is a key organisational objective.