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- 1. Application of Data Science in Healthcare Anurati Kulkarni 2MAECO 2237040
- 2. Big Data in Health • Big data has been explored in many different ways, from business to transportation, which has made us aware of how essential it is to daily life. • It has changed the medical industry, and has helped in structuring and mapping large amounts of data to fundamentally alter how even the most basic health monitoring operations are carried out.
- 4. Educated Decision Making • The competitive need for valuable information in the health market is the most significant of the many variables that make data science essential to healthcare today. • Consumers may receive better-quality healthcare with the aid of the correct data collection procedures. • To make educated decisions on the health conditions of their patients, everyone from doctors to health
- 5. Use In Pharma • The foundation for drug synthesis utilizing artificial intelligence is provided by data science, which is its main contribution to the pharmaceutical sector. • Compounds are created that address the statistical association between the attributes using mutation profiling and patient metadata.
- 6. Diagnostics • Applications of data science in healthcare can facilitate and accelerate diagnosis. • In addition to helping with early health issue diagnosis, patient data analysis also enables the creation of medical heatmaps that show the demographic trends in disease.
- 7. Predictive Analysis • Data science predictive models correlate and correlate every data point to symptoms, routines, and diseases. Helps to: • Manage chronic diseases • Monitor and analyze the demand for pharmaceutical logistics • Predict future patient crisis • Deliver faster hospital data documentation
- 8. Imaging • Healthcare professionals often use various imaging techniques like X-Ray, MRI, and CT Scan to visualize your body's internal systems and organs. • Deep learning and image recognition technologies in health Data Science allow detection of minute deformities in these scanned images, helping doctors plan an effective treatment strategy.
- 9. Conclusion • The examples of data science in healthcare above demonstrate how big data will soon be the dominant force in the medical field. • Naturally, such a large potential has hazards, such as the lack of standardized data and ineffective data management rules. • Yet by establishing some norms for data science applications in healthcare and trying to improve the tools used for data analytics in healthcare to make them as error-free as possible, such difficulties can be solved.