International Journal of Science, Engineering and TechnologyAuthors: Neetu Maurya, Ashirwad Kr, Amit, Mohit, Anmol
Abstract: Healthcare systems worldwide are under mounting pressure to shift from reactive treatment models to proactive, data-informed care. Delayed diagnoses, fragmented patient records, and the sheer volume of clinical data produced daily make it increasingly difficult for clinicians to act on time-sensitive information. This paper presents an AI-Driven Predictive Healthcare Analytics Platform that combines machine learning, natural language processing, and real-time patient monitoring to anticipate disease progression and flag high-risk patients before their conditions deteriorate. The platform ingests structured data from electronic health records (EHRs), unstructured clinical notes, laboratory results, and wearable sensor streams, then applies ensemble learning models—including gradient-boosted trees and deep recurrent networks—to generate individualized risk scores. A clinical decision support dashboard surfaces these predictions in plain language, enabling physicians, nurses, and care coordinators to intervene at the right moment without wading through raw data. The system was evaluated across three hospital departments—cardiology, internal medicine, and emergency care—on a retrospective dataset of 84,000 patient records spanning five years. It achieved a predictive accuracy of 91.7% for adverse events within a 48-hour window and reduced clinician alert fatigue by consolidating actionable warnings into a single prioritized feed. The platform is designed to comply with HIPAA and HL7 FHIR standards, runs on commodity cloud infrastructure, and integrates with major EHR vendors through a RESTful API layer. Beyond its immediate clinical utility, the system lays a scalable foundation for population health management, readmission prevention, and personalised treatment planning. This work demonstrates that thoughtfully engineered AI—grounded in real clinical workflows—can meaningfully support human decision-making without replacing the judgment and empathy that define good medicine.
DOI: http://doi.org/10.5281/zenodo.20570254
