用大模型思路预测未做检验的异常结果，提升早期诊断能力
Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests
- 将检验结果视为标签与值的集合，用序列翻译方式建模
- 在联合预测高尿酸、高血糖等四项指标时AUC最高提升8%
- 无需补全缺失值，直接衔接大模型与医疗表格数据
临床检验结果在诊断中无处不在。基于已做的检验项目预测未检测项目的异常值极具吸引力，可实现全民早期诊断。其中全血细胞计数（CBC）最为常用。将常规生化检验与CBC结合，形成每位患者独特的检验值组合，即带有缺失值的表格。本文将此问题建模为集合翻译任务：源集包含类似GPT的标签嵌入及其对应值，目标集仅含标签嵌入。该方法无需隐式估计缺失值，有效连接大模型与表格数据。应用于临床检验数据，在联合预测高尿酸、高血糖、高胆固醇及低铁蛋白水平时，AUC最高提升8%。
原文摘要 · Abstract (English)
Clinical laboratory results are ubiquitous in any diagnosis making. Predicting abnormal values of not prescribed tests based on the results of performed tests looks intriguing, as it would be possible to make early diagnosis available to everyone. The special place is taken by the Common Blood Count (CBC) test, as it is the most widely used clinical procedure. Combining routine biochemical panels with CBC presents a set of test-value pairs that varies from patient to patient, or, in common settings, a table with missing values. Here we formulate a tabular modeling problem as a set translation problem where the source set comprises pairs of GPT-like label column embedding and its corresponding value while the target set consists of the same type embeddings only. The proposed approach can effectively deal with missing values without implicitly estimating them and bridges the world of LLM with the tabular domain. Applying this method to clinical laboratory data, we achieve an improvement up to 8% AUC for joint predictions of high uric acid, glucose, cholesterol, and low ferritin levels.
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