Downloads: 4 | Views: 30 | Weekly Hits: ⮙4 | Monthly Hits: ⮙4
Informative Article | Engineering Science | India | Volume 9 Issue 5, May 2020
Predictive Analytics for Early Detection and Intervention of Chronic Diseases Using Electronic Health Records
Abstract: Worldwide health systems are grappling with the high impact of chronic illnesses which result in increased rates of sickness and death, alongside soaring health care expenses. The key to effective management of these illnesses lies in early detection and intervention. This paper focuses on employing predictive analytics on electronic health records (EHRs) to aid in the preemptive recognition and management of chronic conditions. The paper proposes the use of a comprehensive EHR dataset with extensive patient details, covering demographic information, clinical observations, test results, and records of medication. I apply sophisticated machine learning techniques, including deep learning and ensemble strategies, to construct models that can predict which patients might face a higher risk of developing chronic conditions such as diabetes, heart disease, and chronic obstructive pulmonary disease (COPD). The efficiency of these predictive models is assessed through specific performance indicators like the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, and specificity. The outcomes from this study could transform how chronic diseases are managed, promoting proactive and personalized intervention strategies. By pinpointing individuals at elevated risk prematurely, health care practitioners can begin preventive measures, lifestyle adjustments, and focused therapies sooner to stave off or delay the onset of chronic diseases. This method is expected to enhance patient health outcomes, lower health care costs, and minimize the strain on global health systems. The paper adds to the expanding knowledge base in predictive analytics within healthcare, showcasing the efficacy of using EHR data for early detection and intervention in chronic diseases. The predictive models and insights generated from this study can be incorporated into clinical decision support systems, enabling healthcare professionals to make educated decisions and offer tailored care to patients prone to chronic illnesses.
Keywords: predictive analytics, early detection, chronic diseases, electronic health records, machine learning, deep learning, ensemble methods, risk factors, biomarkers, early intervention, patient outcomes, healthcare costs, interpretability, personalized interventions, preventive measures, lifestyle modifications, targeted treatments, clinical decision support systems, personalized care
Edition: Volume 9 Issue 5, May 2020,
Pages: 1847 - 1857