ARTIFICIAL INTELLIGENCE (AI)-POWERED PREDICTIVE MODELING FOR PATIENT READMISSION AND TREATMENT RESPONSE USING ELECTRONIC HEALTH RECORDS AND MACHINE LEARNING - Muzammil Ahmad Khan; Aysha Ijaz Khan; Mashooque Ali Mahar; Husnain Saleem; Amna Asif (Spectrum of Engineering Sciences, 2025)
Predictive modeling using electronic health records (EHRs) and machine learning can revolutionize the medical field by exposing high-risk patients and refining treatment strategies. Objective: Using EHRs and machine learning techniques, this research seeks to develop and evaluate AI-powered predictive models for patient readmission and treatment response. Methods: Using a quasi-experimental study design, the effectiveness of AI-powered predictive models in projecting patient readmission and treatment response was assessed. There were 200 adults, age (≥18 years), in the sample. Carried out in a hospital setting, the study uses electronic health records (EHRs) and allows evaluation of AI-driven predictive algorithms in a real clinical environment. Electronic health records (EHRs) are the primary data source for the study. EHRs give extensive data on patient demographics, treatment outcomes, and medical history. Descriptive statistics; logistic regression; machine learning algorithms (random forest, support vector machine); model performance evaluation using metrics such accuracy, precision, recall, and area under the receiver operating characteristic curve AUC-ROC. Results: The model identified significant readmission risk factors with an 85% accuracy rate. Conclusions: By identifying high-risk individuals and fine-tuning treatment protocols, AI-powered predictive modeling has demonstrated its ability to improve patient outcomes.The findings suggest that clinical decision support systems providing personalized recommendations for patient care could be developed using artificial intelligence.