Dividing patients into similar groups plays a significant role in implementing personalized care. Clinicians and researchers have been applying patient grouping techniques in disease phenotyping, risk stratification, and personalized medicine. However, the current approaches are either based on pure domain knowledge where the underlying patient similarity cannot be precisely quantified, or based on unsupervised clustering techniques which completely ignore the clinical context of measuring patient similarity. In the study, we propose an outcome-driven approach to identify clinically similar patients which are grouped together as a precision cohort. The approach quantitatively measures the similarity between patients in terms of a particular clinical outcome of interest, thus patients who have a similar clinical outcome tend to be grouped into the same group. We demonstrate the effectiveness of the approach in a real-world case study: from an atrial fibrillation patient cohort that is usually considered to be at high risk for ischemic stroke (IS), according to current clinical guidelines. Our approach successfully identified a precision cohort of patients with truly low risk of IS.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com