RQ1. What are the key features adapted to integrate structured and unstructured data in healthcare big data domain? |
Big data can handle a plethora of types of data including images, temporal information, EHR data, business and audit relevant data, and many other structural and non-structural data. The prime concern of this research question is to outline the available techniques proposed in the literature to handle structured and unstructured data in healthcare domains |
RQ2. What are different techniques proposed to provide an easy and timely data-access interface for doctors? |
Big data comprises a gigantic amount of sensitive information. It is highly secure and only authentic person can access it. Normally it takes more time in verification, data providence and in simulations to extract relevant information. And in this over-populated real-life scenario, the caretakers face more patient in less timings. To overcome this problem, how many different techniques suggested and what are the gaps in this solution to be addressed in future are the key concern of this research question |
RQ3. What are different ways to improve communication between the doctor and patient? |
The prime objective of this research question is, to develop an optimal communication model for both doctor and patients by enlisting the communication problems (language barrier, lack of facilities, old age or handicap personnel etc.) in the available systems. This efficient model will change the overall healthcare system |
RQ4. What are different types of classification models proposed for accurate disease diagnosing using patient historical information? |
This question aims to outline multiple IoT-based and machine learning-based disease diagnosing applications suggested by researchers using patient’s historical information. It also aims to discuss each model capabilities based on the information provided in the article |
RQ5. What are different applications of big data analytics in healthcare domain? |
The prime focus of this research question is, to extend big data analytics to new healthcare applications by outlining the current available applications of healthcare big data analytics |