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dc.contributor.author | Nada Munir, 01-135201-079 | |
dc.contributor.author | Ahmar Aamir, 01-135201-008 | |
dc.date.accessioned | 2024-03-11T06:01:03Z | |
dc.date.available | 2024-03-11T06:01:03Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://hdl.handle.net/123456789/17053 | |
dc.description | Supervised by Dr. Fatima Khalique | en_US |
dc.description.abstract | Technology is improving and modifying healthcare systems. Real-time dashboards driven by AI offer a new method of using data to support decision-making for healthcare professionals. There isn’t yet a dashboard that has all the capabilities. Our proposed approach will include hot-spot identification, forecasting, and preprocessing. The dashboard aims to ensure performance and adaptability by utilizing artificial intelligence and machine learning to tackle the difficulties associated with disease surveillance in Pakistan. Pakistan lags in using AI for health-related analytic and disease surveillance, despite the fact that data analysis has substantially assisted public health professionals worldwide. In order to find trends in geographic data, the dashboard will make use of the Gi* model for hotspot discovery. The dashboard will forecast using ARIMA and Facebook Prophet models to identify trends across various time periods. By providing users with powerful AI-powered analytic tools, the dashboard can help them identify patterns and trends that would be difficult to detect manually | en_US |
dc.language.iso | en | en_US |
dc.publisher | Computer Sciences | en_US |
dc.relation.ispartofseries | BS (IT);P-02160 | |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Health Informatics | en_US |
dc.subject | Analytical Dashboard | en_US |
dc.title | Artificial Intelligence Based Health Informatics Analytical Dashboard "Health Lens" | en_US |
dc.type | Project Reports | en_US |