Bayesian information-theoretic approach to determine effective scanning protocols of cancer patients
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The aspect of limited temporal data is one of the many challenges when dealing with clinical data. The amount of data that can be practically collected in everyday patients during the therapy is very limited due to the financial cost and the patient’s burden. This motivates us to transfer the mathematical and computational models to meet the challenges in clinical data, before we use them to guide patient therapy via prediction. In this talk, I will discuss modeling approaches to tackle this problem. For instance, I will discuss a Bayesian information-theoretic approach to determine effective scanning protocols of cancer patients. We propose a modified mutual information function with a temporal penalty term to account for the loss of temporal data. The effectiveness of our framework is demonstrated in determining scanning scheduling for radiotherapy and androgen suppression therapy patients.
