Patient clustering using dynamic partitioning on correlated and uncertain biomedical data. (July 2020)

• Record Type: Journal Article
• Authors: Forkan, Abdur Rahim Mohammad; Khalil, Ibrahim; Kumarage, Heshan
• Is Part Of: Computer methods and programs in biomedicine. Volume 190(2020)
• Publication Date: 2020-07
• Subjects: Vital sign -- Patient clustering -- Correlations -- Uncertainty -- Healthcare; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105483
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We developed a novel patient clustering method using vital sign data. We developed an unsupervised pattern discovery method for correlated data. We have utilized uncertainty value for data portioning in out clustering process. Our clustering scheme can combine patients with similar medical conditions. Abstract: Background and objectives Health professionals look for specific patterns by correlating multiple physiological data in the process of deciding treatments to remedy clinical abnormalities. Biomedical data exhibit some common patterns in the event of identical clinical illnesses. The primary interest of this work is automatic discovery of such patterns in vital sign data (e.g. heart rate, blood pressure) using unsupervised learning and utilising them to identify patients with similar clinical conditions. Methods A patient clustering method is developed that efficiently isolates patients into multiple groups by discovering dynamic patterns in multi-dimensional vital sign data. A dynamic partitioning algorithm and a patient clustering approach is proposed by introducing a measure namely aggregated instance-wise uncertainty (AIU) computed from multi-dimensional physiological time-series data. Results The developed model is evaluated qualitatively using principal component analysis and silhouette value; and quantitatively in terms of its ability of clustering patients associated with different clinical situations. Experiments are conducted using real-world biomedical data of patients having various clinical conditions. Thee observed accuracy was 82.85% and 91.17% on two experimental datasets comprised of 35 and 34 patients data respectively.The comparisons show that the proposed approached outperformed than other methods in state-of-the-art approach. Conclusions The experimental outcomes demonstrate the effectiveness of the proposed approach in discovering distinct patterns with predictive significance.