DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data
Ruilin Wang, Bo-Hong Wang, Elizabeth Kourbatski, Jun Bai, Hegang Chen, Ziyang Song, Gilles Boire, Marie Hudson, Yue Li
cs.AI, cs.LG, cs.MA
Submitted: 2026-08-05
Comments: 34 pages, 5 figures
License: http://creativecommons.org/licenses/by/4.0/
The gist: Clinical machine learning (ML) has the potential to support high-stakes medical decision-making, but reliable deployment is often constrained by scarce, heterogeneous, and temporal complexity.
Terminology
Abstract
Clinical machine learning (ML) has the potential to support high-stakes medical decision-making, but reliable deployment is often constrained by scarce, heterogeneous, and temporal complexity. Developing effective ML pipelines for such data remains time-consuming and error-prone, while existing automated machine learning (AutoML) systems only partially address this challenge because they largely rely on brute-force search over predefined spaces and lack explicit reasoning and memory. We therefore reformulate AutoML for small clinical data from exhaustive search to reasoning-driven refinement. We propose DoctorAgents, an agentic AI framework that autonomously constructs and optimizes end-to-end ML pipelines through specialized large language model (LLM) agents for generation, validation, and refinement. DoctorAgents backpropagates natural-language feedback through textual gradient descent to perform targeted updates without exhaustive search. Experiments across diverse clinical tasks show that DoctorAgents consistently outperforms established AutoML baselines while producing more interpretable task-specific representations.
Sources
- GPT-4 Technical Report
- Interpolation-Prediction Networks for Irregularly Sampled Time Series
- Multi-Time Attention Networks for Irregularly Sampled Time Series
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- gpt-oss-120b & gpt-oss-20b Model Card
- Evaluating Large Language Models Trained on Code
- The Llama 3 Herd of Models
- ReAct: Synergizing Reasoning and Acting in Language Models
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