Toward Digital Twins in the Intensive Care Unit: A Medication Management Case Study
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Objective
To evaluate the efficacy of digital twins developed using a large language model (LLaMA-3), fine-tuned with Low-Rank Adapters (LoRA) on ICU physician notes, and to determine whether specialty-specific training enhances treatment recommendation accuracy compared to other ICU specialties or zero-shot baselines.
Materials and Methods
Digital twins were created using LLaMA-3 fine-tuned on discharge summaries from the MIMIC-III dataset, where medications were masked to construct training and testing datasets. The medical ICU dataset (1,000 notes) was used for evaluation, and performance was assessed using BERTScore and ROUGE-L. A zero-shot baseline model, relying solely on contextual instructions without training, was also evaluated. While our approach moves toward digital twin capabilities, it does not incorporate real-time, patient-specific EHR data and can be viewed as an ICU specialty-level language model adaptation.
Results
Models fine-tuned on medical ICU notes achieved the highest BERTScore (0.842), outperforming models trained on other specialties or mixed datasets. Zero-shot models showed the lowest performance, highlighting the importance of training.
Discussion
The findings demonstrate that specialty-specific training significantly improves treatment recommendation accuracy in digital twins compared to generalized or zero-shot approaches. Tailoring models to specific ICU domains strengthens their clinical decision-support capabilities.
Conclusion
Context-specific fine-tuning of large language models is crucial for developing effective digital twins, offering foundational insights for personalized clinical decision support.
