LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health Records
This work addresses a domain-specific problem for healthcare by providing a more accurate and generalizable framework for lab test prediction, though it is incremental as it builds on language modeling approaches.
The paper tackles the problem of predicting lab test outcomes from electronic health records to reduce patient burden and improve availability, proposing LabTOP, a unified model that outperforms existing methods on three datasets by performing continuous numerical predictions for diverse lab items.
Lab tests are fundamental for diagnosing diseases and monitoring patient conditions. However, frequent testing can be burdensome for patients, and test results may not always be immediately available. To address these challenges, we propose LabTOP, a unified model that predicts lab test outcomes by leveraging a language modeling approach on EHR data. Unlike conventional methods that estimate only a subset of lab tests or classify discrete value ranges, LabTOP performs continuous numerical predictions for a diverse range of lab items. We evaluate LabTOP on three publicly available EHR datasets and demonstrate that it outperforms existing methods, including traditional machine learning models and state-of-the-art large language models. We also conduct extensive ablation studies to confirm the effectiveness of our design choices. We believe that LabTOP will serve as an accurate and generalizable framework for lab test outcome prediction, with potential applications in clinical decision support and early detection of critical conditions.