Year: 2022
Type: review
Abstract: Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.
Authors: Tyler J. Loftus, Benjamin Shickel, Jeremy A. Balch, Patrick J. Tighe, Kenneth L. Abbott, Brian Fazzone, Erik M. Anderson, Jared Rozowsky, Tezcan Ozrazgat-Baslanti, Yuanfang Ren, Scott A. Berceli, William R. Hogan, Philip A. Efron, J. Randall Moorman, Parisa Rashidi, Gilbert R. Upchurch, Azra Bihorac
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,150
Funders: National Science Foundation, National Institutes of Health, National Institute on Aging, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, National Center for Advancing Translational Sciences
Awards: G1171417540, G1317102655, Together: Transforming and Translating Discovery to Improve Health, G2463208549, Aligning Patient Acuity with Intensity of Care after Surgery, G2992120421, G3066885995, G3345260134, G3611247453, UF Clinical and Translational Science Award, G483334175, G6671297155, G7295051578, CAREER: Fundamental Intelligent Building Blocks of the Intensive Care Unit (ICU) of the Future, University of Florida Older Americans Independence Center (OAIC), Explainable, Fair, Reproducible and Collaborative Surgical Artificial Intelligence: Integrating data, algorithms and clinical reasoning for surgical risk assessment (XAI-IDEALIST), ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention, G912448
OpenAlex ID: W4291150331