AI-Based Clinical Decision Support Framework for Health Report Visual Analytics

Abstract

Healthcare systems generate a large volume of clinical reports containing laboratory results, diagnostic observations, and patient information. These reports are often unstructured and require manual interpretation by clinicians, which can be time-consuming and prone to human error. This paper proposes an AI-based Clinical Decision Support Framework designed to automatically analyze health reports and generate meaningful insights through visual analytics. The proposed system integrates Natural Language Processing (NLP) techniques for extracting clinical entities from medical reports, Random Forest algorithms for disease risk prediction, and open Large Language Models (LLMs) for generating contextual health insights. The framework processes patient reports in multiple formats such as PDF, CSV, and Electronic Health Records (EHR), converts unstructured medical data into structured features, and performs automated health analysis. The extracted results are presented through interactive visual dashboards including charts, trend graphs, and heatmaps to support clinicians in identifying abnormalities and making informed decisions. Experimental evaluation demonstrates that the system improves the efficiency of medical report interpretation and enhances decision support by providing automated insights and predictive analytics. The proposed framework contributes toward intelligent healthcare systems by combining machine learning, NLP, and visual analytics for improved clinical decision-making.