Future Research Direction of Big Data Analytics in Healthcare 2023-2024.pdf
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Future Research Direction of Big Data Analytics in Healthcare 2023-2024.pdf
- 1. An Academic presentation by Dr. Nancy Agnes, Head, Technical Operations, Tutors India Group www.tutorsindia.com Email: info@tutorsindia.com
- 2. Big Data refers to handling and analysing extensive datasets. Big Data is characterised by the following 3 ‘Vs’: What is Big Data? Volume: which refers to the extensive quantities of data, Velocity: which refers to creating data in real-time, and Variety: Big Data can be structured, semi-structured and unstructured
- 3. What is Big Data Analytics? Big Data Analytics (BDA) is the process of analysing Big Data to obtain important information like associations, market trends and consumer preferences, facilitating data-driven and evidence-based decision- making.
- 4. Big Data Analytics in Healthcare Big Data and Big Data Analytics have gained significant interest in the healthcare sector since it is a complex system with multiple stakeholders such as health insurers, government agencies and researchers. Novel technologies like Big Data have enabled capturing information about individual patients over a longer time. Electronic Health Records (EHRs) can be considered an ideal example of Big Data in healthcare. The available research on the applications of Big Data Analytics in medicine and healthcare are as follows:
- 6. Image Processing: contd... Medical images are valuable sources of data that are commonly used for diagnosis, therapy evaluation and planning. The size of medical image data can range from a few megabytes for a single study (e.g. histology images) to hundreds of megabytes per study. Big Data Analytics helps in storing such massive amounts of data, which subsequently assist healthcare professionals in the diagnosis, treatment and prognosis of a condition.
- 7. Signal Processing: contd... Signal Processing, also called biomedical signal processing, deals with obtaining and preprocessing physiological signals and drawing inferences to identify patterns and trends within the signals. Examples of signal processing in healthcare include Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG). Like medical images, signals pose challenges, especially while acquiring high-resolution signals and storage from multiple monitors connected to the same patient. This calls for research on the application of Big Data Analytics in signal processing, which will enable storing massive amounts of data.
- 8. Predicting and diagnosing diseases: contd... There has been research on incorporating Big Data Analytics in the personalised detection of diabetes in the form of smart clothing. Big Data Analytics can also be used in molecular profiling. EHR data and molecular data can be used for predicting diseases.
- 9. Future Research Directions for Big Data Analytics in Healthcare : contd... Prediction of outbreaks and identification of the causes and the pattern of spread of infectious diseases Integration and standardisation of medical devices. Improving the security and privacy of patient information. Developing cost-effective solutions for the incorporation of Big Data in healthcare. Improving the regulatory framework for Big Data Analytics in healthcare.
- 10. Conclusion Big Data refers to handling and analysing huge amounts of data and is characterised by volume, variety and velocity. An advanced type of analytics for analysing Big Data is known as Big Data Analytics. Big Data and Big Data Analytics have gained considerable interest in the healthcare sector, where they can be used for predicting diseases, image processing and signal processing. Future research on Big Data Analytics involves research on protecting the security of patient information, making the incorporation of Big Data more cost-effective, improving the regulatory framework, standardisation and integration of medical devices and predicting outbreaks and identifying the causes and spread of infectious diseases.
- 11. About Tutors India We are a team of professional academic writers, statisticians and researchers from recognised universities who assist Master’s students with dissertations. We understand that students often face challenges with dissertation statistical analysis. Therefore, we offer comprehensive solutions that help them analyse data and draw meaningful conclusions, which is crucial for their dissertation. In addition, we also offer study guides and examples that help Master’s students write their dissertations with confidence.
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