We propose a new approach to clinical decision support with interpretable recommendations from multi-view data. We introduce a Bayesian network structure learning method to help identify the relevant factors and their relationships. Guided by minimal domain knowledge, this method highlights the significant associations among the demography, medical and family history, lifestyle, and biomarker data to facilitate informed clinical decisions. We demonstrate the effectiveness of the method for detecting mild neurocognitive disorder in the elderly from a real-life dataset in Singapore. The empirical results show that our method achieves better interpretability in addition to comparable accuracy with respect to the benchmark studies.