Accurate principal discharge diagnoses are fundamental for effective morbidity surveillance, yet healthcare systems worldwide are plagued by imprecise and non-specific coding, hindering public health analysis and resource allocation. This article introduces a novel computational model designed to address this challenge by automatically predicting specific discharge diagnoses from a combination of structured and unstructured clinical data. Our approach utilizes a dual-pipeline architecture to process heterogeneous information streams. The structured data pipeline systematically extracts diagnostic indicators from laboratory results, pharmacy records, and surgical procedure codes (CUPS). Concurrently, the unstructured data pipeline employs a sophisticated multi-stage natural language processing (NLP) framework to interpret free-text radiology reports and clinical notes. This NLP pipeline first uses a fine-tuned bidirectional encoder representations from transformers (BERT) model to accurately detect and filter out negated or uncertain statements. Subsequently, a Sentence-Transformer model performs semantic similarity analysis to match affirmative clinical findings against a curated, expert-validated knowledge base of diagnostic sentences. For novel or complex cases not covered by the knowledge base, the system leverages the generative capabilities of GPT-3.5 Turbo as a fallback mechanism. The model is implemented as a scalable, service-oriented application using a MySQL database and a FastAPI framework, ensuring seamless integration with existing hospital electronic health record (EHR) systems. Performance was rigorously evaluated using a custom 0-5 scoring metric, developed in collaboration with a thoracic surgery specialist and validated by a team of three hospital epidemiologists, to assess clinical accuracy. Results demonstrate high performance in identifying both primary and secondary diagnoses, with notable success in common critical conditions like pulmonary embolism. Furthermore, the model shows significant potential for scalability, as performance on rare conditions such as epidural hematoma improved substantially with the addition of only a few expert-defined diagnostic patterns. This open-source computational tool offers a robust and economically viable solution for healthcare institutions to enhance the precision of their diagnostic coding, thereby improving the quality of morbidity data without requiring disruptive changes to established clinical workflows.