Abstract
The paper presents a proposed clinical intelligence system, which is called NLP-Bayesian Optimization Clinical Model (NBO-CM), to study large-scale unstructured electronic health record narratives in the MIMIC-IV discharge and radiology note datasets with the help of a structured pipeline of clinical text preprocessing, feature extraction and probabilistic modeling to solve the linguistic variability, missing information and uncertainty in clinical decisions. Text preprocessing methods are first applied in the standardization of clinical narratives, such as text tokenization, text normalization, text lemmatization, text segmentation, and text negation detection. Next, the unstructured text is converted into structured clinical variables with the help of feature extraction techniques, including named entity recognition, medical concept normalization using UMLS/SNOMED ontologies, generating contextual embeddings, and vectorizing text using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. Bayesian Networks are then used to model these features, with dependency structure learning based on the Poor-and-Rich Optimization algorithm (PRO), having the ability to explore probabilistic relationships efficiently, and parameter estimation based on expectation–maximization, providing robust learning with incomplete and uncertain conditions of data. Lastly, probabilistic reasoning and inference are used to predict diseases, prioritize risks and make clinical inferences with clear measures of uncertainty and interpretability. Experimental analysis of real-world MIMIC-IV clinical notes indicates that the framework is more effective in terms of diagnostic accuracy, predictive strength, and clinical explainability than traditional machine learning methods, resulting in a scaled and explainable framework of intelligent clinical decision support systems in complex care settings.