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.
authors: Suman Bhoi Mong-Li Lee Wynne Hsu Hao Sen Andrew Fang Ngiap Chuan Tan
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