CLIP-H
Clinical hypothesis verification on MIMIC-IV using sparse autoencoders and an LLM ensemble.
- Software Engineer, AI Research
- Purdue University
- 2026
- Research
0.844
Problem
Language models will generate clinical hypotheses all day. The hard question is which ones survive contact with the data, and whether you can show your work well enough for a reviewer to check it.
Approach
I built CLIP-H with Purdue and Harvard Business School faculty. The goal was hypothesis verification you could actually audit: sparse features you can name, an ensemble that disagrees usefully, and a validation setup that doesn't quietly grade its own homework.
What I built
A hypothesis verification pipeline over MIMIC-IV using Top-K sparse autoencoders to surface interpretable features, with a GPT and Claude ensemble scoring candidate hypotheses.
Architecture
Top-K sparse autoencoders over MIMIC-IV representations, producing features sparse enough to be named and inspected.
A GPT and Claude ensemble scoring candidate clinical hypotheses, where disagreement between models is signal rather than noise.
Validation against a synthetic oracle with known ground truth, so verification accuracy is measurable rather than asserted.
Impact
Reached 0.844 AUROC against the synthetic oracle and certified 14 hypotheses with Purdue and Harvard Business School faculty. The work is being prepared for a NeurIPS submission targeted for September 2026.
0.844