Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests
This work addresses early diagnosis for patients by leveraging common lab data, but it is incremental as it applies a novel method to a known bottleneck in tabular modeling.
The paper tackles the problem of predicting abnormal values of unprescribed clinical tests from routine test results, achieving up to 8% AUC improvement in joint predictions for conditions like high uric acid and glucose.
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.