B-S.B.75: Can AI detect people most in need of a blood-based checkup? Development and prospective validation of AI models for the prediction of future abnormal clinical laboratory tests in the Finnish population.

Author

Kira Detrois

[sponser-meet-now-chat][/sponser-meet-now-chat]

Contact Us

Clinical laboratory tests are essential tools to monitor overall health and can reveal early signs of serious health conditions. While considerable work has been done on applying artificial intelligence to electronic health record (EHR) data for disease risk prediction, the prediction of abnormal laboratory measurements remains underexplored. To address this gap, we developed models based on EHR data to predict future abnormal measurements for 4 clinically actionable clinical laboratory tests: HbA1C, TSH, eGFR, and LDL; and are prospectively validating the predictions with a recall study where we invite individuals for follow-up testing. We trained XGBoost models, as well as transformer encoder-based models capturing the longitudinal trends of the lab measurements, to predict abnormal values separately for each test type. We included individuals aged 30-70 as part of the FinnGen study (N=310,526) without a history of the relevant diseases. Predictors included individuals' age and sex, BMI, education level, prior laboratory measurements, diagnoses, and medication purchase information. The final XGBoost models reached AUCs ranging from 0.78 (TSH) to 0.91 (eGFR) and average precisions from 0.14 (TSH) to 0.30 (LDL). Prior laboratory measurements were the strongest predictors, substantially improving the models compared to baselines with age, sex, and BMI. While for eGFR, the transformer models provided significant additional improvements. Our work demonstrates that both tabular- and longitudinal-based methods can predict future abnormal lab measurements using EHR data, highlighting their potential use in targeted screening, early detection, and preventive interventions. Co-authors: Zhijian Yang, Maxim Lamoureux, Leena Viiri, Andrea Ganna