Data Mining to Determine Risk in Medical Decisions

Demonstrates statistical techniques that can be used to investigate matters of risk that include kernel density estimation, predictive modeling, association rules and text analysis. This title discusses some of the flaws in models which may lead to misleading results.

Decisions regarding the risks involved in medical treatments must belong to patients and their physicians – after all, it is the patient′s health and life which is at stake. But patients will not be equipped for this decision-making process if they cannot be given some idea as to the risks and benefits of treatment. Such risks are generally estimated by a consensus panel of specialist physicians using supporting medical literature. Unfortunately, this literature does not always provide a good estimate of risk, particularly in the case of rare occurrences. This book demonstrates statistical techniques that can be used to investigate matters of risk. These include kernel density estimation, predictive modeling, association rules and text analysis. It also shows, through example, how these techniques can provide meaningful results, and examines current methods, discussing some of the flaws in models which may lead to misleading results. After a general introduction to the concept of medical risk, the subjects covered include the process by which rare occurrences are investigated in drugs or treatments, the trade-offs between risks and benefits, extrapolation of clinical trial results and the cost of healthcare in relation to risks. It also examines problems such as competing risks, error, and the use of group identities, as well as looking at the issue of futility. The book concludes with a chapter providing a general discussion and summary, and an appendix shows some of the processes for using SAS Enterprise Miner to perform some of the models used in the text.