Comorbidity: a multidimensional approach.
Comorbidity represents an extremely complex domain of research. An individual entity, the patient, is the center of gravity of a system characterized by multiple, complex, and interrelated conditions, disorders, or diseases. Such complexity is influenced by uncertainty that is difficult to decipher and is proportional to the number of associated morbidities. Computational scientists usually provide meta-analysis studies aimed at integrating various types of evidence, but in our opinion they may help reformulate comorbidity by emphasizing, in particular, two aspects: (i) a systems approach, which allows for an ensemble view of comorbidity, and offers a model representation generalizable to multimorbidity; and (ii) a dynamic network inference approach, which is indicated for the analysis of links among morbidities and evaluation of risk. Notably, the main question remains whether such instruments suggest a shift of paradigm providing prospective impact on medical practice. We have identified in the simultaneous consideration of multiple dimensions linked to comorbidity complexity the rationale for such translation.
Center for Computational Science (CCS), Miller School of Medicine, University of Miami, Miami, FL 33136, USA; Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology (IFC), National Research Council (CNR), Pisa 56124, Italy.
Enrico
E
Capobianco
Pietro
P
Lio'
D015897:Comorbidity; D019295:Computational Biology / Q000379:methods; D004194:Disease; D006801:Humans; D010361:Patients; D049490:Systems Biology; D057170:Translational Research, Biomedical
clustering; comorbidity; dynamic mapping; inference; multidimensionality; patient disease network