Abstract
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.
Keywords
- Humans and AI: Personalization and User Modeling
- Multidisciplinary Topics and Applications: Health and Medicine
