A treatment recommendation tool based on temporal data mining and an automated dynamic database to record evolving data
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The thesis examines sequential mining approaches in the context of treatment recommendation for Gliblastoma (GBM) patients. GBM is the most lethal and biologically the most aggressive forms of brain tumor with median survival of approximately 1 year. A significant challenge in treating such rare forms of cancer is to make the best decision about optimal treatment plans for patients after standard of care. We tailor the existing sequential mining approaches by adding constraints to mine significant treatment options for cancer patients. The goal of the work is to analyze which treatment patterns play a role in prolonging the survival period of patients. In addition to the treatment analysis, we also discover some interesting clinical and genomic factors, which influence the survival period of patients. A treatment advisor tool has been developed based on the predictive features discovered. This tool is used to recommend treatment guidelines for a new patient based on the treatments meted out to other patients sharing clinical similarity with the new patient. The recommendations are also guided by the influential treatment patterns discovered in the study. The tool is based on the notion of patient similarity and uses a weighted function to calculate the same. The recommendations made by the tool may influence the clinicians to have the patients record some vital data on their own. With the progression of the treatment the clinicians may want to add to or modify some of the vital data elements previously decided to be recorded. In such a case a static database would not be very efficient to record the data since manual intervention is inevitable to incorporate the changes in the database structure. To solve this problem we have developed a dynamic database evolution framework, which uses a form based interface to interact with the clinician to add or modify the data elements in a database. The clinicians are flexible to create a new form for patients or modify existing forms based on a patient’s condition. As a result, appropriate schema modifications would be done in the relational database at the backend to incorporate these changes maintaining relational consistency.