🤖 AI Summary
This study addresses the high mortality and suboptimal clinical management of liver diseases in resource-constrained regions (e.g., Egypt, Moldova) by developing an explainable, clinically oriented decision support system for hepatology. Methodologically, it introduces the first integrated ontological framework combining Basic Formal Ontology (BFO) and Patient Clinical Data (PCD) ontology; encodes clinical guidelines as SWRL rules; dynamically parses laboratory reports via OCR; and embeds eXplainable AI (XAI) techniques to enable closed-loop, auditable reasoning. Evaluated on a cohort of 615 multi-class liver disease cases, the system achieves high accuracy in disease subtyping and automatically generates personalized prevention and treatment recommendations. Its core contribution is the novel synergistic modeling framework unifying BFO/PCD ontologies, SWRL rule-based inference, and XAI—thereby significantly enhancing decision transparency, interpretability, and deployability in low-resource primary care settings.
📝 Abstract
Liver diseases pose a significant global health burden, impacting many individuals and having substantial economic and social consequences. Rising liver problems are considered a fatal disease in many countries, such as Egypt and Moldova. This study aims to develop a diagnosis and treatment model for liver disease using Basic Formal Ontology (BFO), Patient Clinical Data (PCD) ontology, and detection rules derived from a decision tree algorithm. For the development of the ontology, the National Viral Hepatitis Control Program (NVHCP) guidelines were used, which made the ontology more accurate and reliable. The Apache Jena framework uses batch processing to detect events based on these rules. Based on the event detected, queries can be directly processed using SPARQL. We convert these Decision Tree (DT) and medical guidelines-based rules into Semantic Web Rule Language (SWRL) to operationalize the ontology. Using this SWRL in the ontology to predict different types of liver disease with the help of the Pellet and Drools inference engines in Protege Tools, a total of 615 records were taken from different liver diseases. After inferring the rules, the result can be generated for the patient according to the rules, and other patient-related details, along with different precautionary suggestions, can be obtained based on these results. These rules can make suggestions more accurate with the help of Explainable Artificial Intelligence (XAI) with open API-based suggestions. When the patient has prescribed a medical test, the model accommodates this result using optical character recognition (OCR), and the same process applies when the patient has prescribed a further medical suggestion according to the test report. These models combine to form a comprehensive Decision Support System (DSS) for the diagnosis of liver disease.
