The broad adoption of electronic health records (EHRs) has led to vast amounts of data being accumulated on a patient’s history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. However, existing medication recommendation systems have yet to make use of all these information sources in a seamless manner, and they do not provide a justification on why a particular medication is recommended. In this work, we design a two-stage personalized medication recommender system called PREMIER that incorporates information from the EHR. We utilize the various weights in the system to compute the contributions from the information sources for the recommended medications. Our system models the drug interaction from an external drug database and the drug co-occurrence from the EHR as graphs. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Case studies demonstrate that the justifications provided by PREMIER are appropriate and aligned to clinical practices.
Chronic disease management involves frequent administration of invasive lab procedures in order for
clinicians to determine the best course of treatment
regimes for these patients. However, patients are
often put off by these invasive lab procedures and
do not follow the appointment schedules. This has
resulted in poor management of their chronic conditions leading to unnecessary disease complications. An AI system that is able to personalize the
prediction of individual patient lab test responses
will enable clinicians to titrate the medications to
achieve the desired therapeutic outcome. Accurate prediction of lab test response is a challenge because these patients typically have co-morbidities and their treatments might influence the target lab test response. To address this,
we model the complex interactions among different medications, diseases, lab test response, and
fine-grained dosage information to learn a strong
patient representation. Together with information
from similar patients and external knowledge such
as drug-lab interactions and diagnosis-lab interaction, we design a system called KALP to perform
personalized prediction of patients’ response for a
target lab result and identify the top influencing
factors for the prediction. Experiment results on
real-world datasets demonstrate the effectiveness of
KALP in reducing prediction errors by a significant
margin. Case studies show that the identified factors are consistent with clinicians’ understanding.
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