TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Yue Wang Yingzhou Lu Yinlong Xu Zihan Ma Hongxia Xu Bang Du Honghao Gao Jian Wu
Recently, there has been a burgeoning interest in virtual clinical trials,
which simulate real-world scenarios and hold the potential to significantly
enhance patient safety, expedite development, reduce costs, and contribute to
the broader scientific knowledge in healthcare. Existing research often focuses
on leveraging electronic health records (EHRs) to support clinical trial
outcome prediction. Yet, trained with limited clinical trial outcome data,
existing approaches frequently struggle to perform accurate predictions. Some
research has attempted to generate EHRs to augment model development but has
fallen short in personalizing the generation for individual patient profiles.
Recently, the emergence of large language models has illuminated new
possibilities, as their embedded comprehensive clinical knowledge has proven
beneficial in addressing medical issues. In this paper, we propose a large
language model-based digital twin creation approach, called TWIN-GPT. TWIN-GPT
can establish cross-dataset associations of medical information given limited
data, generating unique personalized digital twins for different patients,
thereby preserving individual patient characteristics. Comprehensive
experiments show that using digital twins created by TWIN-GPT can boost
clinical trial outcome prediction, exceeding various previous prediction
approaches. Besides, we also demonstrate that TWIN-GPT can generate
high-fidelity trial data that closely approximate specific patients, aiding in
more accurate result predictions in data-scarce situations. Moreover, our study
provides practical evidence for the application of digital twins in healthcare,
highlighting its potential significance.