wtf is medical-ai-experiments?

This project is a personal research workspace where the author, an AI researcher, tries to build machine learning models that outperform current published results on specific medical tasks. The focus is on analyzing biological signal data, starting with detecting irregular heartbeats and working toward identifying seizures from brain wave recordings. The author plans to apply techniques from an area of AI called "attention," which helps models focus on the most relevant parts of a large dataset. In this context, that might mean identifying brief but important patterns in a long recording of someone's heartbeat or brain activity. The repository is explicitly marked as a work in progress, so it is an evolving experiment rather than a finished tool. The likely audience is other researchers or developers interested in medical machine learning, particularly those working with time-series data from healthcare sensors. For example, a data scientist exploring new approaches for arrhythmia detection might look at this repository to see what techniques the author is trying and whether those approaches achieve better accuracy than existing published methods. It could also serve as a reference for anyone curious about how attention-based AI concepts translate to medical signals. The README is sparse, so there is little detail on the specific datasets, model architectures, or results. It reads more like a statement of intent than documentation. Because it is a personal experiments repository, anyone looking at it should expect incomplete work and frequent changes rather than a polished, ready-to-use solution. The value here is in watching the experimental process, not deploying a finished product.