基于稀缺训练数据集的计算机模拟临床试验量子生成模型
A quantum generative model for in silico clinical trials using scarce training datasets
AI总结：
针对训练样本稀缺的问题，本研究提出一种量子生成模型，在IBM Heron r2超导量子计算机上运行，相比经典模型在泛化性和表达性上更优，可用于生成高保真计算机模拟患者以辅助临床试验。
AI中文摘要：
计算机模拟方法已成为补充临床试验的策略，对传统方法成本高或难以应用的罕见病或异质性疾病尤为重要。经典生成模型在大量数据库训练时生成高保真数据能力极强，但在训练样本稀缺时表现不佳。本研究利用量子计算机表示复杂概率分布的潜力生成高保真计算机模拟患者，提出可将不对称数据库整合为量子电路的流程，该电路作为量子生成模型。我们以包含7个临床变量的骨髓增生异常综合征（MDS）患者数据库为概念验证评估方案有效性，在IBM Heron r2超导量子计算机（编号“ibm_basquecountry”）上运行该量子生成模型，并与知名经典基线模型对比。结果显示，该量子生成模型在泛化性和表达性指标上优于经典生成模型，表明其具备为临床试验生成高保真计算机模拟患者的潜在有效性。
英文摘要：
In silico methods have emerged as a strategy to complement clinical trials. These are particularly relevant for rare or heterogeneous diseases for which traditional methods are costly or difficult to apply. While classical generative models have shown an extremely good ability to generate high fidelity data when trained using extensive databases, they often struggle when the available samples for training are scarce. In this work, we leverage the potential of quantum computers to represent complex probability distributions to generate high fidelity in silico patients. We propose a pipeline able to combine asymmetric databases into a quantum circuit that serves as a quantum generative model. We evaluate the efficacy of our proposal using a database of Myelodysplastic Syndrome (MDS) patients with 7 clinical variables as a proof-of-concept. We executed our quantum generative model in the IBM Heron r2 ``ibm\_basquecountry'' superconducting quantum computer and compare our method with well known classical baselines. Our results show that the quantum generative model surpasses the classical generative models in generalization and expressivity metrics, indicating its potential validity to generate high fidelity in silico patients for clinical trials.
