In an ideal medical environment, real-time coagulation monitoring can enable early detection and prompt remediation of risks. However, traditional Thromboelastography (TEG), a widely employed diagnostic modality, can only provide such outputs after nearly 1 hour of measurement. The delay might lead to elevated mortality rates. These issues clearly point out one of the key challenges for medical AI development: Mak-ing reasonable predictions based on very small data sets and accounting for variation between different patient populations, a task where conventional deep learning methods typically perform poorly. We present Physiological State Reconstruc-tion (PSR), a new algorithm specifically designed to take ad-vantage of dynamic changes between individuals and to max-imize useful information produced by small amounts of clini-cal data through mapping to reliable predictions and diagnosis. We develop MDFE to facilitate integration of varied temporal signals using multi-domain learning, and jointly learn high-level temporal interactions together with attentions via HLA; furthermore, the parameterized DAM we designed maintains the stability of the computed vital signs. PSR evaluates with 4 TEG-specialized data sets and establishes remarkable perfor-mance -- predictions of R2 > 0.98 for coagulation traits and error reduction around half compared to the state-of-the-art methods, and halving the inferencing time too. Drift-aware learning suggests a new future, with potential uses well be-yond thrombophilia discovery towards medical AI applica-tions with data scarcity.

翻译：在理想的医疗环境中，实时凝血监测能够实现风险的早期发现与及时干预。然而，作为广泛应用的诊断方法，传统血栓弹力图（TEG）需要近1小时的测量才能提供相应结果。这种延迟可能导致死亡率升高。这些问题明确指出医疗人工智能发展的关键挑战之一：基于极小数据集进行合理预测，并考虑不同患者群体间的差异——传统深度学习方法在此类任务中通常表现不佳。我们提出生理状态重建（PSR）算法，该算法专门设计用于利用个体间的动态变化，通过将少量临床数据映射为可靠预测与诊断，从而最大化其有效信息。我们开发了MDFE模块，通过多域学习促进多源时序信号的融合，并借助HLA联合学习高层时序交互与注意力机制；此外，我们设计的参数化DAM模块保持了计算生命体征的稳定性。PSR在4个TEG专用数据集上进行了评估，展现出卓越性能——凝血特征预测的R2 > 0.98，与现有最优方法相比误差降低约50%，推理时间亦缩短一半。漂移感知学习展现了新的前景，其潜在应用不仅限于血栓性疾病检测，更可拓展至其他数据稀缺的医疗人工智能领域。