Building

The future

AjaX Skein

Skein partnered with King’s College London for research and innovation project in AI-Enabled Clinical Decision Making.

Data infrastructure
HealthTech
Personal data
Research & Analytics
Web
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What we have done / The process

In our endeavor to redefine the valuation of health data through a computer science lens, we embarked on a project that integrated three key domains: collaborative machine learning, network analysis of clinical decision-making, and the principles of Human-Centered Computing.

The foundation of our approach was an innovative collaborative machine learning algorithm. This paradigm extended beyond traditional data processing, embracing a dynamic learning process capable of handling the complex and evolving nature of healthcare datasets. Our algorithm was designed to extract meaningful patterns and insights from large-scale health data, contributing to a more nuanced understanding of patient care and outcomes.

Central to our methodology was the application of network analysis to the realm of clinical decision-making. This involved constructing and examining complex networks to visualize and analyze the interdependencies and information flow among healthcare professionals. By applying network theory, we were able to uncover the intricate dynamics of clinical decision processes, thereby identifying opportunities for optimization and efficiency enhancement in healthcare delivery.

Human-Centered Computing principles were intricately woven into every aspect of our technology. Recognizing the critical role of human factors in healthcare, our design philosophy centered on creating a user-centric tool. This involved ensuring our technology was not only intuitive and accessible to healthcare practitioners but also respectful of patient privacy and inclusive of diverse patient needs. Our adherence to these principles ensured that the technology augmented human decision-making in healthcare, rather than supplanting it.

Our work represents a significant contribution to the field of computer science, particularly in the application of machine learning and network analysis to healthcare data. By harmoniously integrating these domains, our technology not only enhanced the value derived from health data but also propelled forward the concept of patient-centered, data-driven healthcare solutions. This project stands as a testament to the potential of interdisciplinary approaches in addressing complex challenges at the intersection of healthcare and computer science.

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