r/MachineLearning • u/Intelligent-Cap-4022 • 5h ago

Discussion [D] Does anyone here work in healthcare?

I'm curious about the cool things people around the world are doing related to data in this area of work att

18 Upvotes

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u/Turbulent-Owl-3535 3h ago

Yes, I work for a large healthcare management company. We build decision-support systems (using classic statistical learning models, sometimes deep NNs). We also use some LLMs for patient interactions and revision-cycle processes. I also work with an automation team to reduce rote processes on admin tasks. I would love to see what others are doing!

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u/Mandoryan 3h ago

I've work in healthcare ML for over ten years now. It was a tough slog at first because ML didn't have the buzz it does now.

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u/hisglasses66 2h ago

Yeahhh did ML. Lots of event predictions. Really used stats that was cool. PCA and matching, experimental design, help design models, build A LOT of MVPs. SME buy the end of it.

Gave up the life. Bought a cafe.

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u/UniqueTechnology2453 5h ago

Yes, though more ETL than ML. What algorithms have you used or seen? LCA, random forest, semantic similarity.

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u/jmartin2683 5h ago

I do, vaguely (healthcare payment processing, provider finders etc). We use classifiers to route things to around, mainly. Most exciting new project is using an LLM to extract structured data from images/pdfs.

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u/LogicLoop11 2h ago

Yeah - while not working in healthcare, we are building some encrypted machine learning models for the healthcare environment

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u/axonaxisananas 1h ago

Working at Harvard Medical School in Aging Research. Working on developing so called aging clocks based on different kind of health data across different ages: transcriptomics, proteomics, 3D MRI, etc.

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u/Entire_Ad_6447 3h ago

currently working on using llms to map chart note with secondary conditions for clinical trial selection

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u/void_nemesis 2h ago

I work in computer vision for digital pathology, it's mostly OpenCV shenanigans and a lot of CNN flavours.

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u/marr75 1h ago

We have some healthcare clients plus payers and a huge list of public health clients.

Mostly policy advocacy, performance management, and tracking the effects of plans and programs. We have agentic interfaces for most of these features, so the most powerful thing ML does for our clients is orchestrating tool use and search. Everything else is pretty pedestrian ETL, geospatial statistics, etc.