用常规化验历史序列提升阿尔茨海默病早期诊断准确率
Utilizing Sequential Information of General Lab-test Results and Diagnoses History for Differential Diagnosis of Dementia
- 将化验记录视作时间序列'句子'，用嵌入技术捕捉检验项目间隐含关系
- 基于LSTM与Transformer模型，识别患者长期化验趋势变化，准确率显著提升
- 无需特殊检测，适合在资源有限的基层医疗场景中推广使用
阿尔茨海默病（AD）的早期诊断面临多重数据挑战：患者数据差异大、专用检测手段难获取、过度依赖单一指标。这些挑战因AD的渐进性而加剧——病理变化可能在临床症状出现前数十年即已发生。为此，本研究提出一种新方法，利用常规实验室检验历史进行早期检测与鉴别诊断。通过将检验序列建模为“句子”，采用词嵌入技术捕捉检验项目间的潜在关联，并结合长短期记忆网络（LSTM）和Transformer等深度时序模型，挖掘患者记录中的动态模式。实验表明，该方法显著提升了诊断准确性，支持在多样临床环境中实现可扩展、低成本的AD筛查。
原文摘要 · Abstract (English)
Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges, including high variability in patient data, limited access to specialized diagnostic tests, and overreliance on single-type indicators. These challenges are exacerbated by the progressive nature of AD, where subtle pathophysiological changes often precede clinical symptoms by decades. To address these limitations, this study proposes a novel approach that takes advantage of routinely collected general laboratory test histories for the early detection and differential diagnosis of AD. By modeling lab test sequences as "sentences", we apply word embedding techniques to capture latent relationships between tests and employ deep time series models, including long-short-term memory (LSTM) and Transformer networks, to model temporal patterns in patient records. Experimental results demonstrate that our approach improves diagnostic accuracy and enables scalable and costeffective AD screening in diverse clinical settings.
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