  We propose CHiLL (Crafting High-Level Latents), an approach for
natural-language specification of features for linear models. CHiLL prompts
LLMs with expert-crafted queries to generate interpretable features from health
records. The resulting noisy labels are then used to train a simple linear
classifier. Generating features based on queries to an LLM can empower
physicians to use their domain expertise to craft features that are clinically
meaningful for a downstream task of interest, without having to manually
extract these from raw EHR. We are motivated by a real-world risk prediction
task, but as a reproducible proxy, we use MIMIC-III and MIMIC-CXR data and
standard predictive tasks (e.g., 30-day readmission) to evaluate this approach.
We find that linear models using automatically extracted features are
comparably performant to models using reference features, and provide greater
interpretability than linear models using "Bag-of-Words" features. We verify
that learned feature weights align well with clinical expectations.

Computation and Language;Artificial Intelligence;Machine Learning