TrialSynth: Generation of Synthetic Sequential Clinical Trial Data
<|reference_start|>TrialSynth: Generation of Synthetic Sequential Clinical Trial Data: Analyzing data from past clinical trials is part of the ongoing effort to optimize the design, implementation, and execution of new clinical trials and more efficiently bring life-saving interventions to market. While there have been recent advances in the generation of static context synthetic clinical trial data, due to both limited patient availability and constraints imposed by patient privacy needs, the generation of fine-grained synthetic time-sequential clinical trial data has been challenging. Given that patient trajectories over an entire clinical trial are of high importance for optimizing trial design and efforts to prevent harmful adverse events, there is a significant need for the generation of high-fidelity time-sequence clinical trial data. Here we introduce TrialSynth, a Variational Autoencoder (VAE) designed to address the specific challenges of generating synthetic time-sequence clinical trial data. Distinct from related clinical data VAE methods, the core of our method leverages Hawkes Processes (HP), which are particularly well-suited for modeling event-type and time gap prediction needed to capture the structure of sequential clinical trial data. Our experiments demonstrate that TrialSynth surpasses the performance of other comparable methods that can generate sequential clinical trial data, in terms of both fidelity and in enabling the generation of highly accurate event sequences across multiple real-world sequential event datasets with small patient source populations when using minimal external information. Notably, our empirical findings highlight that TrialSynth not only outperforms existing clinical sequence-generating methods but also produces data with superior utility while empirically preserving patient privacy.<|reference_end|>
@article{gao2024trialsynth:,
    title={TrialSynth: Generation of Synthetic Sequential Clinical Trial Data},
    author={Chufan Gao, Mandis Beigi, Afrah Shafquat, Jacob Aptekar, Jimeng Sun},
    journal={arXiv preprint arXiv:2409.07089},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07089},
    primaryClass={cs.LG}
}