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System Architecture of A European Platform for Health Policy Decision Making: MIDAS
Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research
Visualization for machine learning (VIS4ML) research aims to help experts
apply their prior knowledge to develop, understand, and improve the performance
of machine learning models. In conceiving VIS4ML systems, researchers
characterize the nature of human knowledge to support human-in-the-loop tasks,
design interactive visualizations to make ML components interpretable and
elicit knowledge, and evaluate the effectiveness of human-model interchange. We
survey recent VIS4ML papers to assess the generalizability of research
contributions and claims in enabling human-in-the-loop ML. Our results show
potential gaps between the current scope of VIS4ML research and aspirations for
its use in practice. We find that while papers motivate that VIS4ML systems are
applicable beyond the specific conditions studied, conclusions are often
overfitted to non-representative scenarios, are based on interactions with a
small set of ML experts and well-understood datasets, fail to acknowledge
crucial dependencies, and hinge on decisions that lack justification. We
discuss approaches to close the gap between aspirations and research claims and
suggest documentation practices to report generality constraints that better
acknowledge the exploratory nature of VIS4ML research
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