Speaker
Description
Basic research and clinical medicine are increasingly capitalizing on data-driven approaches to derive insights into disease pathophysiology and discover new therapeutic targets. While advanced algorithms are readily available, their application requires a combination of domain, quantitative, and technical expertise, leaving them out of reach for many domain expert researchers and clinicians. The rtemis framework is a collection of packages that has been designed to make advanced machine learning, visualization, and agentic AI as efficient, flexible and accessible as possible.
The framework takes advantage of the S7 class system to provide a fully object-oriented backend including comprehensive type-checking and validation of all inputs and outputs, maximizing correctness and robustness. The user-facing API is functional and designed to offer the most user-friendly and efficient experience, significantly reducing the time and effort required to train and evaluate models regardless of user technical expertise. All algorithm configuration for supervised and unsupervised learning is performed using setup functions that generate S7 config objects, validated at input and providing comprehensive error messages.
A custom text formatting system supports output of ANSI-formatted text in the console and HTML-formatted text in web interfaces, as well as plain text output for logging. All messaging is handled by a custom logging system that includes datetime stamps, color-coded output and provenance. This provides essential transparency for complex pipelines where a single user call results in a complex cascade of internal function calls.
The framework has been successfully used for algorithm development, applied health data science, and machine learning training.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | machine learning, visualization, agentic AI, S7 |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |