Heuristics in Medical Data Mining

Abstract

Deriving—or discovering—information from data has come to be known as data mining. Within health care, the knowledge from medical mining has been used in tasks as diverse as patient diagnosis (Brameier et al., 2000; Mani et al., 1999; Cao et al., 1998; Henson et al., 1996), inventory stock control (Bansal et al., 2000), and intelligent interfaces for patient record systems (George at al., 2000). It has also been a tool of medical discovery itself (Steven et al., 1996). Yet, it remains true that medicine is one of the last areas of society to be “automated,” with a relatively recent increase in the volume of electronic data, many paper-based clinical record systems in use, a lack of standardisation (for example, among coding schemes), and still some reluctance among health-care providers to use computer technology. Nevertheless, the rapidly increasing volume of electronic medical data is perhaps one of the domain’s current distinguishing characteristics, as one of the last components of society to be “automated.” Data mining presents many challenges, as “knowledge” is automatically extracted from data sets, especially when data are complex in nature, with many hundreds of variables and relationships among those variables that vary in time, space, or both, often with a measure of uncertainty, as is common within medicine. Cios and Moore (2001) identified a number of unique features of medical data mining, including the use of imaging and need for visualisation techniques, the large amounts of unstructured nature of free text within records, data ownership and the distributed nature of data, the legal implications for medical providers, the privacy and security concerns of patients requiring anonymous data used, where possible, together with the difficulty in making a mathematical characterisation of the domain. Strictly speaking, many ventures within medical data mining are better described as exercises in “machine learning,” where the main issues are, for example, discovering the complexity of relationships among data items, or making predictions in light of uncertainty, rather than “data mining,” in large, possibly distributed, volumes of data that are also highly complex. Large data sets mean not only increased algorithmic complexity but also often the need to employ special-purpose methods to isolate trends and extract “knowledge” from data. However, medical data frequently provide just such a combination of vast (often distributed) complex data sets.