A.I- Driven Predictive Analytics in Managing Exacerbation Risks in Chronic Respiratory Disease Patients

@article{1706664,
      author = {Oluwatobi Anthonia Ogunfuye},
      title = {A.I- Driven Predictive Analytics in Managing Exacerbation Risks in Chronic Respiratory Disease Patients},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {339-348},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706664.pdf},
      abstract = {Chronic respiratory diseases (CRDs), including asthma and chronic obstructive pulmonary disease (COPD), present significant global health challenges due to their high prevalence, complex management requirements, and susceptibility to acute exacerbations. Traditional approaches to managing these conditions rely on reactive measures, resulting in suboptimal patient outcomes and escalating healthcare costs. This paper explores the transformative potential of artificial intelligence (AI)-driven predictive analytics in reshaping CRD management. By leveraging diverse datasets encompassing genetic predispositions, environmental exposures, and lifestyle variables, AI models can predict exacerbation risks with unprecedented accuracy, enabling timely and personalized interventions. The study highlights key components of predictive analytics, including the integration of genetic markers, pollution levels, and behavioral factors, and discusses their implications for risk stratification and proactive care. Furthermore, it examines the technical, ethical, and privacy-related challenges that must be addressed to ensure equitable and secure deployment of these technologies. Reviewing current research, the paper emphasizes how AI-driven models can optimize treatment plans, enhance patient adherence, and significantly reduce hospitalizations and associated costs.  This work also identifies future directions for advancing AI in CRD management, emphasizing the need for more stringent algorithms, real-time health monitoring integration, and interdisciplinary collaboration. By embracing these innovations, the healthcare system can transition toward a more proactive, patient-centered, and cost-effective paradigm for managing chronic respiratory diseases.},
      keywords = {Chronic Respiratory Diseases (CRDs), Asthma, Chronic Obstructive Pulmonary Disease (COPD), Artificial Intelligence (AI), Predictive Analytics, Exacerbation Risk Prediction, Proactive Healthcare, Personalized Medicine, Machine Learning Models, Genetic Markers, Environmental Factors, Lifestyle Variables, Healthcare Cost Reduction, Real-time Health Monitoring, Ethical Considerations},
      month = {December},
  }