Building block system of health care data analytics

Building block system of healthcare big data analytics involves a few steps Burkle et al. (2001):

• What is the purpose that the new data will and should serve
  • How many functions should it support
  • Marking which parts of that new data is needed for each function
• Identify the tool needed to support the purpose of that new data
• Create a top level architecture plan view
• Building based on the plan but leaving room to pivot when needed
  • Modifications occur to allow for the final vision to be achieved given the conditions at the time of building the architecture.
  • Other modifications come under a closer inspection of certain components in the architecture

With big data analytics in healthcare, parallel programming and distributive programming are part of the solution to consider in building a network cluster (Mirtaheri, Khaneghah, Sharifi, & Azgomi, 2008; Services, 2015). Distributed programming allows for connecting multiple computer resources distributed across different locations (Mirtaheri et al., 2008). Programming that is maximizing the connections for processing or accessing data that is distributed across the computational resources is considered as parallel programming (Mirtaheri et al., 2008; Saden, 2011).  Burkle et al. (2001) explained how they used the building block design for a DNA network cluster build a system to help classify/predict what genome they are analyzing to a pathogen and understand which part of the genome found in many pathogens may be immune to certain treatments:

• Tracing data from sequencer (*.esd file)
  • Base caller
• “Raw” sequence (*.scf file)
  • Edit and assemble and export genome assembly for research
• “Clean” sequence
  • External references are called in and outputted from reference databases
• Genus and species are identified
• Completed results are calling and outputting into an attributed local database

The process above flow for sequencing genomic data is part of the top-level plan that was modified as time went by, thus step four of the building blocks process.

Now, let’s consider using the building blocks system for healthcare systems, on a healthcare problem that wants to monitor patient vital signs similar to Chen et al. (2010).

• The purpose that the new data will serve: Most hospitals measure the following vitals for triaging patients: blood pressure and flow, core temperature, ECG, carbon dioxide concentration (Chen et al. 2010).
  1. Functions should it serve: gathering, storing, preprocessing, and processing the data. Chen et al. (2010) suggested that they should also perform a consistency check, aggregating and integrate the data.
  2. Which parts of the data are needed to serve these functions: all
• Tools needed: distributed database system, wireless network, parallel processing, graphical user interface for healthcare providers to understand the data, servers, subject matter experts to create upper limits and lower limits, classification algorithms that used machine learning
• Top level plan: The data will be collected from the vital sign sensors, streaming at various time intervals into a central hub that sends the data in packets over a wireless network into a server room. The server can divide the data into various distributed systems accordingly. A parallel processing program will be able to access the data per patient per window of time to conduct the needed functions and classifications to be able to provide triage warnings if the vitals hit any of the predetermined key performance indicators that require intervention by the subject matter experts.  If a key performance indicator is sparked, send data to the healthcare provider’s device via a graphical user interface.
• Pivoting is bound to happen; the following can happen
  1. Graphical user interface is not healthcare provider friendly
  2. Some of the sensors need to be able to throw a warning if they are going bad
  3. Subject matter experts may need to readjust the classification algorithm for better triaging

Thus, the above problem as discussed by Chen et al. (2010), could be broken apart to its building block components as addressed in Burkle et al. (2011).  These components help to create a system to analyze this set of big health care data through analytics, via distributed systems and parallel processing as addressed by Services (2015) and Mirtaheri et al. (2008).

References

• Burkle, T., Hain, T., Hossain, H., Dudeck, J., & Domann, E. (2001). Bioinformatics in medical practice: what is necessary for a hospital?. Studies in health technology and informatics, (2), 951-955.
• Chen, B., Varkey, J. P., Pompili, D., Li, J. K., & Marsic, I. (2010). Patient vital signs monitoring using wireless body area networks. In Bioengineering Conference, Proceedings of the 2010 IEEE 36th Annual Northeast (pp. 1-2). IEEE.
• Mirtaheri, S. L., Khaneghah, E. M., Sharifi, M., & Azgomi, M. A. (2008). The influence of efficient message passing mechanisms on high performance distributed scientific computing. In Parallel and Distributed Processing with Applications, 2008. ISPA’08. International Symposium on (pp. 663-668). IEEE.
• Sandén, B. I. (2011-01-14). The design of Multithreaded Software: The Entity-Life Modeling Approach, 1st Edition. [Bookshelf Online].
• Services, E. E. (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 1st Edition. [Bookshelf Online].