The Future Ocean: Final report = Ozean der Zukunft: Abschlussbericht

2006 - 201

Visual Analytics for Performing Complex Tasks with Electronic Health Records

Electronic health record systems (EHRs) facilitate the storage, retrieval, and sharing of patient health data; however, the availability of data does not directly translate to support for tasks that healthcare providers encounter every day. In recent years, healthcare providers employ a large volume of clinical data stored in EHRs to perform various complex data-intensive tasks. The overwhelming volume of clinical data stored in EHRs and a lack of support for the execution of EHR-driven tasks are, but a few problems healthcare providers face while working with EHR-based systems. Thus, there is a demand for computational systems that can facilitate the performance of complex tasks that involve the use and working with the vast amount of data stored in EHRs. Visual analytics (VA) offers great promise in handling such information overload challenges by integrating advanced analytics techniques with interactive visualizations. The user-controlled environment that VA systems provide allows healthcare providers to guide the analytics techniques on analyzing and managing EHR data through interactive visualizations. The goal of this research is to demonstrate how VA systems can be designed systematically to support the performance of complex EHR-driven tasks. In light of this, we present an activity and task analysis framework to analyze EHR-driven tasks in the context of interactive visualization systems. We also conduct a systematic literature review of EHR-based VA systems and identify the primary dimensions of the VA design space to evaluate these systems and identify the gaps. Two novel EHR-based VA systems (SUNRISE and VERONICA) are then designed to bridge the gaps. SUNRISE incorporates frequent itemset mining, extreme gradient boosting, and interactive visualizations to allow users to interactively explore the relationships between laboratory test results and a disease outcome. The other proposed system, VERONICA, uses a representative set of supervised machine learning techniques to find the group of features with the strongest predictive power and make the analytic results accessible through an interactive visual interface. We demonstrate the usefulness of these systems through a usage scenario with acute kidney injury using large provincial healthcare databases from Ontario, Canada, stored at ICES

Balancing macrophage activation in health and disease:The epigenetic, transcriptional and immunometabolic insights

Macrophages are crucial components of the innate immune system. Macrophages manifest extreme heterogeneity in response to the local milieu. The plasticity and diversity of macrophages are pivotal for host defense against pathogenic insults and the maintenance of tissue homeostasis. However, dysregulated macrophage activation can lead to acute and chronic inflammatory disorders, such as COVID-19, atherosclerosis, and inflammatory bowel diseases. The activation states of macrophages are shaped by various mechanisms, such as epigenetic modifications, transcriptional regulation, and metabolic alterations. This dissertation provides comprehensive transcriptomic and metabolomic profiles of widely used macrophage models. We build a macrophage activation classifier and identify key regulatory network modules constructed by unbiased approaches. Furthermore, our data delivers insights into targeting metabolic pathways, kinases, and epigenetic enzymes for therapeutic development against various diseases. These findings advance our knowledge in macrophage activation and pathogenesis of different diseases and disorders that can support future studies from basic science to translational medicine and from bench to bedside