A Learning Health System for Radiation Oncology

The proposed research aims to address the challenges faced by clinical data science researchers in radiation oncology accessing, integrating, and analyzing heterogeneous data from various sources. The research presents a scalable intelligent infrastructure, called the Health Information Gateway and Exchange (HINGE), which captures and structures data from multiple sources into a knowledge base with semantically interlinked entities. This infrastructure enables researchers to mine novel associations and gather relevant knowledge for personalized clinical outcomes. The dissertation discusses the design framework and implementation of HINGE, which abstracts structured data from treatment planning systems, treatment management systems, and electronic health records. It utilizes disease-specific smart templates for capturing clinical information in a discrete manner. HINGE performs data extraction, aggregation, and quality and outcome assessment functions automatically, connecting seamlessly with local IT/medical infrastructure. Furthermore, the research presents a knowledge graph-based approach to map radiotherapy data to an ontology-based data repository using FAIR (Findable, Accessible, Interoperable, Reusable) concepts. This approach ensures that the data is easily discoverable and accessible for clinical decision support systems. The dissertation explores the ETL (Extract, Transform, Load) process, data model frameworks, ontologies, and provides a real-world clinical use case for this data mapping. To improve the efficiency of retrieving information from large clinical datasets, a search engine based on ontology-based keyword searching and synonym-based term matching tool was developed. The hierarchical nature of ontologies is leveraged to retrieve patient records based on parent and children classes. Additionally, patient similarity analysis is conducted using vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) to identify similar patients based on text corpus creation methods. Results from the analysis using these models are presented. The implementation of a learning health system for predicting radiation pneumonitis following stereotactic body radiotherapy is also discussed. 3D convolutional neural networks (CNNs) are utilized with radiographic and dosimetric datasets to predict the likelihood of radiation pneumonitis. DenseNet-121 and ResNet-50 models are employed for this study, along with integrated gradient techniques to identify salient regions within the input 3D image dataset. The predictive performance of the 3D CNN models is evaluated based on clinical outcomes. Overall, the proposed Learning Health System provides a comprehensive solution for capturing, integrating, and analyzing heterogeneous data in a knowledge base. It offers researchers the ability to extract valuable insights and associations from diverse sources, ultimately leading to improved clinical outcomes. This work can serve as a model for implementing LHS in other medical specialties, advancing personalized and data-driven medicine

Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R

Big data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work.N/

Issue Mapping for an Ageing Europe

Issue Mapping for an Ageing Europe is a seminal guide to mapping social and political issues with digital methods. The issue at stake concerns the imminent crisis of an ageing Europe and its impact on the contemporary welfare state. The book brings together three leading approaches to issue mapping: Bruno Latour's social cartography, Ulrich Beck's risk cartography and Jeremy Crampton's critical neo-cartography. These modes of inquiry are put into practice with digital methods for mapping the ageing agenda, including debates surrounding so-called 'old age', cultural philosophies of ageing, itinerant care workers, not to mention European anti-ageing cuisine. Issue Mapping for an Ageing Europe addresses an urgent social issue with new media research tools

Structured grid generation for gas turbine combustion systems

Commercial pressures to reduce time-scales encourage innovation in the design and analysis cycle of gas turbine combustion systems. The migration of Computational Fluid Dynamics (CFD) from the purview of the specialist into a routine analysis tool is crucial to achieve these reductions and forms the focus of this research. Two significant challenges were identified: reducing the time-scale for creating and solving a CFD prediction and reducing the level of expertise required to perform a prediction. The commercial pressure for the rapid production of CFD predictions, coupled with the desire to reduce the risk associated with adopting a new technology led, following a review of available techniques, to the identification of structured grids as the current optimum methodology. It was decided that the task of geometry definition would be entirely performed within commercial Computer Aided Design (CAD) systems. A critical success factor for this research was the adoption of solid models for the geometry representation. Solids ensure consistency, and accuracy, whilst eliminating the need for the designer to undertake difficult, and time consuming, geometry repair operations. The versatility of parametric CAD systems were investigated on the complex geometry of a combustion system and found to be useful in reducing the overhead in altering the geometry for a CFD prediction. Accurate and robust transfer between CAD and CFD systems was achieved by the use of direct translators. Restricting the geometry definition to solid models allowed a novel two stage grid generator to be developed. In stage one an initial algebraic grid is created. This reduces user interaction to a minimum, by the employment of a series of logical rules based on the solid model to fill in any missing grid boundary condition data. In stage two the quality of the grid is improved by redistributing nodes using elliptical partial differential equations. A unique approach of improving grid quality by simultaneously smoothing both internal and surface grids was implemented. The smoothing operation was responsible for quality, and therefore reduced the level of grid generation expertise required. The successful validation of this research was demonstrated using several test cases including a CFD prediction of a complete combustion system

The AIDS House: Orphan care and the changing household in Lesotho

HIV/AIDS has brought the connections between care and relatedness into sharp relief. In the midst of social change driven largely by the AIDS epidemic, the house has emerged as the most stable element connecting kin in Lesotho. Houses provide spaces that frame human actions, transform relationships, and reflect the social order. The house is a key crossroads for human movement. It is also the site where physical connections, emotional bonds, and feelings of love and affection are nurtured. Most significantly, it is the site where physical acts of caring take place. Based on extensive ethnographic research, I demonstrate that the house is one of the places where the pressures of AIDS-driven change are most felt because of its role in structuring care. AIDS has intensified the importance of the house as caregiving has become a primary means for shaping relatedness

Personalizing Interactions with Information Systems

Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles the user’s mental model with the underlying information system’s organization. Three tiers of personalization systems are presented, paying careful attention to interaction considerations. These tiers show how progressive levels of sophistication in interaction can be achieved. The chapter also surveys systems support technologies and niche application domains

Advancing self-escape training : a needs analysis based on the National Academy of Sciences report "improving self-escape from underground coal mines."

"This report summarizes a needs analysis and actions taken by NIOSH based on the National Academy of Sciences recommendations specific to advancing self-escape training, with an emphasis on preparing rank-and-file mineworkers for self-escape. This report also provides the foundation for the practical guidance offered in its sister publication, the NIOSH Information Circular (IC) "Self-escape Core Competency Profile: Guidance for Improving Underground Coal Miners' Self-escape Competency" [NIOSH 2023], which offers an evidence-based self-escape competency framework derived from the results of this work." - NIOSHTIC-2NIOSHTIC no. 20067688Suggested citation: NIOSH [2023]. Advancing self-escape training: a needs analysis based on the National Academy of Sciences report, \u201cImproving Self-escape from Underground Coal Mines.\u201d By Hoebbel CL, Bellanca JL, Ryan ME, Brnich MJ. Pittsburgh PA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, DHHS (NIOSH) Publication No. 2023-134, https://doi.org/10.26616/NIOSHPUB2023134