Knowledge Based Bayesian Network Construction Algorithm for Medical Data Fusion to Enhance Services and Diagnosis

Traditional Bayesian networks' algorithms are treating the network construction process as an isolated and autonomous data-driven trial-and-error process and completely ignoring the domain knowledge. In this work we are proposing a new 'Semantically Aware Ontology-Based Bayesian Network construction algorithm' that is knowledge centered instead of data centered. The objective of the new algorithm is to empower patients through improving their self-diagnosis and testing by automatically constructing a set of Ontology-Based Bayesian networks using combination of domain and expert knowledge. The exciting thing about the proposed algorithm is that it uses on 'Saudi-native training data' streamed from the “Unified Medical Record” server and authenticated domain and expert knowledge extracted from the “King Abdulla Encyclopedia” server. A proof-of-concept prototype based on open-source software “Netica” and “Protégé” is implemented and tested. It demonstrates learning of probabilities, network structure and mixes discrete and continuous variables. It imports “Diabetes” patient medical record steams from the “Unified Medical Record” server to be used as training and testing datasets. It also extracts Bayesian data variables from the “King Abdullah Encyclopedia” server to aid in constructing and learning the ontology-based Bayesian networks. The prototype is implemented on an Internet server and can be accessed from medical applications on Smartphones and PDAs. It currently deals with 60 positive “Diabetes” Saudi patients and 60 negative "Diabetes" training cases. The resulting Ontology Bayesian network was tested on another 100 test cases drawn randomly from the 'Unified Medical records' server. An accuracy of diagnosis of 100% was achieved on the test data.