Knowledge Discovery From Datasets

Mathematical models can simulate and predict the behavior of systems. Many biomedical researchers, though, are more comfortable with verbal or visual models of complex phenomena. Various mathematical models for frailty have been reviewed elsewhere (Rockwood et al., 2002). An example of the knowledge discovery from the dataset approach would be the work of Drs. Mitnitski and Rockwood. Using large databases, they developed a frailty index consisting of a number of variables including symptoms, attitudes, illnesses, and function that can be used to predict the likelihood of mortality (Mitnitski et al., 2002).

This approach has been used to produce highly predictive models for certain outcomes associated with frailty but will not necessarily further our understanding of it. The most readably available outcomes for database research—death, disability, and institutionalization—do not arise solely from frailty and/or only capture a subset of all frail individuals. Although the presence of frailty makes death more likely, not all deaths in old age are ''frail'' deaths. Between 20 and 47% of deaths in old age have a frail trajectory (Lunney et al., 2002, et al., 2003). Likewise there is more than one pathway to disability. It can arise in a catastrophic or a progressive manner (Ferrucci et al., 1997). Older individuals with frailty commonly show an insidious development of disability (Gill et al., 2004). Ferrucci found that approximately half (51.6%) of older individuals who developed a severe disability over two to three years did so in a catastrophic manner (Ferrucci et al., 1997). The risk factors and outcomes of the two modes of disability onset are different (Ferrucci et al., 1997; Guralnik et al., 2001). Although most residents in long-term care facilities are frail, only a subset of frail individuals reside in institutions. Defining frailty as being in a long-term care residence would be specific but not sensitive (Rockwood et al., 1996).

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