Intelligent Risk Detection for Healthcare

In healthcare contexts, unfortunately for some diseases, surgery is not always considered a final cure, as it can result in a considerably high rate of disabilities, as well as the possibility of comorbidities; for example, types of cancer and even the development of bowel diseases. Further, there is the direct adverse impact on the patients and their families. Therefore, decision-making regarding such surgeries is multi-faceted and complex. To facilitate the surgical decision making process, we contend that such contexts are appropriate for the application of real time intelligent risk detection decision support. We proffer a suitable solution which combines the application of data mining tools followed by Knowledge Discovery (KD) techniques to score key surgery risk levels, assess surgery risks and thereby help medical professionals to make appropriate decisions. The aim of this study is to improve the outcomes and benefits of surgical interventions and support a healthcare value proposition of excellence for patients, their families, providers, healthcare organizations and society by developing an intelligent risk detection framework to improve surgery decision making processes. While such strategies have been used in other industries (i.e. banking and finance), it appears that this is one of the first studies focused on healthcare contexts. To illustrate the power and potential of Business Intelligent and Data Mining techniques to support healthcare decision making scenarios, the following focuses on the contexts of Congenital Heart Disease (CHD) in children as well as the Orthopaedic context of Hip and Knee interventions.