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We report the development and validation of a data-driven real-time riskscore that provides timely assessments for the clinical acuity of ward patientsbased on their temporal lab tests and vital signs, which allows for timelyintensive care unit (ICU) admissions. Unlike the existing risk scoringtechnologies, the proposed score is individualized; it uses the electronichealth record (EHR) data to cluster the patients based on their staticcovariates into subcohorts of similar patients, and then learns a separatetemporal, non-stationary multi-task Gaussian Process (GP) model that capturesthe physiology of every subcohort. Experiments conducted on data from aheterogeneous cohort of 6,094 patients admitted to the Ronald Reagan UCLAmedical center show that our risk score significantly outperforms thestate-of-the-art risk scoring technologies, such as the Rothman index and MEWS,in terms of timeliness, true positive rate (TPR), and positive predictive value(PPV). In particular, the proposed score increases the AUC with 20% and 38% ascompared to Rothman index and MEWS respectively, and can predict ICU admissions8 hours before clinicians at a PPV of 35% and a TPR of 50%. Moreover, we showthat the proposed risk score allows for better decisions on when to dischargeclinically stable patients from the ward, thereby improving the efficiency ofhospital resource utilization.

Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model