R ppt.pdf
- 1. Health-Care Industry and Data Science Anjana Biju
- 2. What is Data Science in Health care? • Data science combines math and statistics, specialized programming, sophisticated analytics, artificial intelligence (AI), machine learning, and specialized subject matter expertise with domain expertise—in this case, healthcare expertise to uncover valuable insights hidden in an organization's data.
- 3. Why Data Science in the Health care Industry? 1. Detection of early symptoms: Healthcare professionals can reduce the rate of treatment failure by making more precise predictions and prescriptions with the help of factual data collected and appropriate analytics. 2. Instantaneous data to support and treat risky diseases: Machine learning methods are also helpful for the detection and monitoring of more widespread illnesses, such as respiratory or cardiac conditions. Clinical trials may benefit from the prediction of prospective diseases made possible by gathering and evaluating data on breathing patterns and heart rate. 3. Facilitates drug development: Accessibility to big data provides lab professionals with the resources to create and test new medications, a process called drug discovery.
- 4. Application of Data Science in Health care 1. Medical Imaging: Medical imaging, or radiography, is the branch of medicine wherein doctors make different images of bodily components for diagnostic or therapeutic purposes. It can be used to detect lung tumors, spine deformities, and so on. Combining artificial intelligence with medical picture analysis improves screening, diagnosis, and prognosis. 2. Pharmaceutical development: The pharmaceutical industry has not found a cure for a vast array of terminal illnesses, such as cancer, AIDS, Alzheimer's, etc. Scientists can better comprehend how specific chemical components may affect the human body by analyzing detailed healthcare data. Therefore, harness the power of AI to create effective drugs.
- 5. 3. Predictive model and Analytics: predictive analytics can be used to identify patterns and trends that can be suggestive of future events. Predictive analytics can be used to estimate the likelihood of particular medical clinical decisions, problems, patterns, and even the spread of infections. Healthcare systems can benefit from using data science by creating predictive modeling and thus improving healthcare. 4. Virtual assistance: A medical virtual assistant specializes in providing medical support services to medical offices and medical professionals, like scheduling appointments, keeping track of patient medical records, and documenting patient contacts. It can be useful to the patients as well to track their appointments, seek medical advice, and many more. 5. Medical Data Privacy and Detecting Fraud: Cyber security is a growing concern. An enormous amount of data is being handled by the healthcare sector. Therefore, it is important to protect data privacy. For the purpose of protecting sensitive data, such as insurance and billing information, data scientists must create frameworks and processes and also be able to spot fraud as soon as possible.
- 6. Future of Data Science in Healthcare • Applications of Data Science in the healthcare system are numerous. The future of data science in healthcare holds immense promise, with advancements in precision medicine, predictive analytics, and personalized treatment plans. Integration of artificial intelligence, machine learning, and continuous health monitoring will revolutionize patient care, enabling more accurate diagnoses, proactive interventions, and improved overall health outcomes.
- 7. References • Pham, Q. (2023, August 2). 7 Applications of data science in healthcare You need to know. Orient Software. https://www.orientsoftware.com/blog/application-of-data-science-in- healthcare/#:~:text=The%20potential%20of%20data%20science,can%2 0then%20be%20more%20personalized. • Vivek, J. (2023, July 3). Data science in healthcare: importance, benefits & tools. Zuci Systems. https://www.zucisystems.com/blog/data-science-in-healthcare/
- 8. THANKYOU