On Dynamic Treatment Regimes: Collaborative Search
and LLM-Driven Decision Trees
Name
gregory-caleg-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
2.92 MB
Format
Adobe PDF
Checksum (MD5)
b619af5c0b564750be9457354f46614a
Author(s)
Gregory, Cale
Advisor(s)
Raskar, Ramesh
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
This thesis evaluates the validity of current dynamic treatment regime algorithms and presents a novel data structure for extracting treatment decisions from unstructured clinical notes. The main contribution is the Clinical Decision Tree (CDT) which uses large language models (LLMs) to extract key decisions in chronic disease treatment. This addresses the main pain points in dynamic treatment regimes of low interpretability and reliance on poorly collected data for traditional machine learning methods. This work contains extensive experiments on mortality prediction, time series forecasting, and synthetic patient modeling. Experiments show that vital-based representations do not capture enough meaningful data about a patient to accurately predict and evaluate new treatment methods. By utilizing latent embeddings and vector search, experiments show that the collected vitals of patients fail to differentiate the outcomes of the related patients. Conversely, the clinical notes contain complex and substantial information about clinical decision making. LLMs enable the valuable knowledge extraction from unstructured data. Utilizing LLMs, experimental results and expert evaluation indicates that CDTs can extract and distill interpretable treatment decisions. Thus, CDTs are a valuable tool that can be refined to increase confidence in treatment decisions and identifying rare and uncommon medical practices.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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