Intelligent Healthcare with Predictive Diagnosis

Abstract

Abstract—The escalating burden on global healthcare systems has intensified the need for rapid, accurate diagnostic tools to mitigate clinician burnout and improve patient outcomes. This paper introduces an Intelligent Healthcare Assistant integrated with Predictive Diagnosis, an advanced framework designed to bridge the gap between initial symptom reporting and clinical intervention. By synthesizing Natural Language Processing (NLP) for symptom extraction with Machine Learning (ML) ensembles for data analysis, the system processes diverse datasets including longitudinal Electronic Health Records (EHR) and real-time physiological metrics. Unlike traditional diagnostic aids, our architecture employs a multi-model ensemble engine that delivers both instantaneous probabilistic assessments and long-term risk forecasting for critical pathologies, such as cardiovascular failure and chronic disease escalation. Furthermore, the system incorporates a closed-loop feedback mechanism that utilizes validated clinical outcomes to iteratively refine model weights, ensuring sustained diagnostic accuracy. Preliminary results suggest that this proactive approach significantly reduces diagnostic latency and democratizes access to early-stage medical insights, providing a scalable solution for overburdened clinical environments. Index Terms—Intelligent Healthcare Assistant, Predictive Analytics, Machine Learning (ML), Clinical Decision Support Systems (CDSS), Electronic Health Records (EHR)

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