This paper, 'Artificial Intelligence (AI), Big Data, and Healthcare', provides a state of the art review of sensors in healthcare, and a projection as to how Artificial Intelligence (AI) along with Big Data can improve and reduce the cost of healthcare. The cost of health care will increase over time because we are living longer, to the point that adults will spend more time caring for their parents than they have for their children. The US Administration of the Aging states that in 2013, the number of persons older than 65 was about 44.7 million or 14% of the US population and that in the year 2060 this number should approximately double. Similar changes are occurring worldwide according to “…by 2050 approximately 20% of the world population will be at least 60 years old”.
Section two of the paper provides a description of some of the current sensors being used, studied and deployed for enhancing the care of patients. Sensors can be active or passive and some can be embedded in a wearer’s clothing, embedded in their bodies, or worn on their wrists connected to their smartphones via radio frequency (RF) connections. There are three types of sensors used within the health care industry: ambient, physiological and biokinetic. Ambient sensors measure the nearby environment, physiological measure human parameters such as glucose levels, and biokinetic sensors measure parameters relative to movement.
Studies are being performed to track patients in their own homes using passive Radio Frequency Identification (RFID) tags. Wearable health devices today include heart monitors, ECG monitors, glucose monitors, pulse oximeters and blood pressure monitors. If we integrate multiple sensors together to add to the Internet of Things (IoT), can we reduce the number of nurses and aides that are required to monitor elderly patients who wish to live alone?
The authors in this white paper believe that we can reduce the cost of healthcare by leveraging 5 technologies, i.e. the IoT, sensors, IPv6, AI, and Big Data. With the advent of Internet Protocol version 6 (IPv6), we will be able to not only monitor every sensor connected to the Internet in almost real time but we will be able to activate some sensors, thereby providing healthcare via AI algorithms at various locations. (Note: IPv6 has128 bits to identify a unique IP address or 3.4 x1038 or 340 Undecillion available addresses.)
If we could capture sensor data from many people on a continuous basis, we could not only look for triggers when sensor data are out of range, but we could build sophisticated signal processing and AI tools to diagnose abnormalities, actuate sensors when and where needed (e.g. deliver medications) and mine data over time. This will allow one to gain more insight into solving known abnormalities, but also discover new relations between measured data and patient ailments. Scientists will be able to perform cause and effect studies, analysis, monitoring, diagnosis, and prediction based upon Big Data analytics, where Big Data relates to volume, velocity, variety, data quality and provenance of data.
To evolve a solution of integrating these 5 technologies, a IoT architecture is provided in section three, using the Cloud Standards Customer Council (CSCC) Cloud Components as a basis for our design. This architecture is described in detail from the Edge Layer to the Enterprise Layer. The described architecture will provide better and cheaper healthcare and allow researchers access to data that are not available today to further their understanding of the human body and its abnormalities.