Mining disease state converters for medical intervention of diseases

In applications such as gene therapy and drug design, a key goal is to convert the disease state of diseased objects from an undesirable state into a desirable one. Such conversions may be achieved by changing the values of some attributes of the objects. For example, in gene therapy one may convert cancerous cells to normal ones by changing some genes' expression level from low to high or from high to low. In this paper, we define the disease state conversion problem as the discovery of disease state converters; a disease state converter is a small set of attribute value changes that may change an object's disease state from undesirable into desirable. We consider two variants of this problem: personalized disease state converter mining mines disease state converters for a given individual patient with a given disease, and universal disease state converter mining mines disease state converters for all samples with a given disease. We propose a DSCMiner algorithm to discover small and highly effective disease state converters. Since real-life medical experiments on living diseased instances are expensive and time consuming, we use classifiers trained from the datasets of given diseases to evaluate the quality of discovered converter sets. The effectiveness of a disease state converter is measured by the percentage of objects that are successfully converted from undesirable state into desirable state as deemed by state-of-the-art classifiers. We use experiments to evaluate the effectiveness of our algorithm and to show its effectiveness. We also discuss possible research directions for extensions and improvements. We note that the disease state conversion problem also has applications in customer retention, criminal rehabilitation, and company turn-around, where the goal is to convert class membership of objects whose class is an undesirable class.