Title: Bayesian Patchworks: An Approach to Case-Based Reasoning,
Abstract: Doctors often rely on their past experience in order to diagnose patients.
For a doctor with enough experience, almost every patient would have
similarities to key cases seen in the past, and each new patient could be
viewed as a mixture of these key past cases. Because doctors often tend to
reason this way, an efficient computationally aided diagnostic tool that thinks
in the same way might be helpful in locating key past cases of interest that
could assist with diagnosis. This article develops a novel mathematical model
to mimic the type of logical thinking that physicians use when considering past
cases. The proposed model can also provide physicians with explanations that
would be similar to the way they would naturally reason about cases. The
proposed method is designed to yield predictive accuracy, computational
efficiency, and insight into medical data; the key element is the insight into
medical data, in some sense we are automating a complicated process that
physicians might perform manually. We finally implemented the result of this
work on two publicly available healthcare datasets, for heart disease
prediction and breast cancer prediction.