Title: PERSONALIZED LAB TEST RESPONSE PREDICTION WITH KNOWLEDGE AUGMENTATION. Abstract: Personalized medical systems are rapidly gaining traction as opposed to “one size
fits all” systems. The ability to predict patients’ lab test responses and provide justification for the predictions would serve as an important decision support tool and
help clinicians tailor treatment regimes for patients. This requires one to model
the complex interactions among different medications, diseases, and lab tests. We
also need to learn a strong patient representation, capturing both the sequential
information accumulated over the visits and information from other similar patients. Further, we model the drug-lab interactions and diagnosis-lab interactions
in the form of graphs and design a knowledge-augmented approach to predict patients’ response to a target lab result. We also take into consideration patients'
past lab responses to personalize the prediction. Experiments on the benchmark
MIMIC-III and a real-world outpatient dataset demonstrate the effectiveness of
the proposed solution in reducing prediction errors by a significant margin. Case
studies show that the identified top factors for influencing the predicted lab results
are consistent with the clinicians' understanding.