Learning Implicit Tasks for Patient-Specific Risk Modeling in ICU

No. 1: Thirty-First AAAI Conference On Artificial Intelligence

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 31

Main Track: Machine Learning Applications

Accurate assessment of the severity of a patientÕs condition plays a fundamental role in acute hospital care such as that provided in an intensive care unit (ICU). ICU clinicians are required to make sense of a large amount of clinical data in a limited time to estimate the severity of a patientÕs condition, which ultimately leads to the planning of appropriate care. The ICU is an especially demanding environment for clinicians because of the diversity of patients who mostly suffer from multiple diseases of various types. In this paper, we propose a mortality risk prediction method for ICU patients. The method is intended to enhance the severity assessment by considering the diversity of patients. Our method produces patient-specific risk models that reflect the collection of diseases associated with the patient. Specifically, we assume a small number of latent basis tasks, where each latent task is associated with its own parameter vector; a parameter vector for a specific patient is constructed as a linear combination of these. The latent representation of a patient, namely, the coefficients of the combination, is learned based on the collection of diseases associated with the patient. Our method could be considered a multi-task learning method where latent tasks are learned based on the collection of diseases. We demonstrate the effectiveness of our proposed method using a dataset collected from a hospital. Our method achieved higher predictive performance compared with a single-task learning method, the Òde facto standard,Ó and several multi-task learning methods including a recently proposed method for ICU mortality risk prediction. Furthermore, our proposed method could be used not only for predictions but also for uncovering patient-specificity from different viewpoints.

10.1609/aaai.v31i1.10766