Bayesian Patchworks: An Approach to Case-Based Reasoning
This work addresses the need for computationally efficient diagnostic tools that provide interpretable insights for doctors, though it appears incremental in applying case-based reasoning to medical data.
The authors tackled the problem of automating case-based reasoning for medical diagnosis by developing a novel mathematical model that mimics physicians' logical thinking, achieving predictive accuracy on heart disease and breast cancer datasets.
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.