Discussions

So I have been knee-deep in a project involving patient outcome prediction models, and let me just say—data can be so annoying and beautiful at the same time. Like, one day you’re feeling like a genius because your model is showing 90% accuracy. The next? You realize your sample was biased AF and now you’re questioning your whole approach.

Been playing around with Voyance for a bit, trying to test a couple of new data prep workflows and it’s honestly made things smoother. Like, I didn’t have to fight with five different Jupyter notebooks just to clean and merge my sources. Also, the no-code options? Lifesaver when your brain is already mush from staring at variables all day.

That said, I’m still learning big time. Especially with healthcare data, there’s always that fine line between getting meaningful insights and overfitting to the mess. I had a mentor once tell me “dirty data is the norm, not the exception” and wow, he wasn’t kidding.

Random side note, I’ve got a friend working on their final thesis and they were freaking out over their lit review. They were talking about getting some nursing dissertation help because apparently, nobody teaches you how to actually write that stuff just how to collect data and stress about it.

Anyway, if anyone here is doing predictive modeling on healthcare datasets (especially in real-time environments), I’d love to hear what kind of metrics or validation tricks you’re using. And if you’ve had success with Voyance in clinical decision support tools drop your wisdom. I’m all ears.

Alright, back to debugging. Peace.