The U.S. Centers for Disease Control is using big data tools to look for health patterns. In one breakthrough, reported by Datanami, the health agency has shown a pattern with opioid misuse, HIV and needle sharing in particular areas of the U.S. Now the CDC is using big data techniques to understand the macro mechanics of the HIV needle-tipped spread. From such analysis it is hoped health prevention can be better targeted and the spread can subsequently be slowed down.
Big data refers to data sets that are so large or complex that traditional data processing applications cannot adequately deal with them. In many walks of life, data sets are becoming increasingly large and this brings with it new challenges for the analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. One such area is with health and here the primary U.S. health agency has recruited technology specialists to help process complex health data trends.
The analysis of health has revealed a problem in Indiana and a surge in cases of infection with the Human Immunodeficiency Virus (HIV). The cause of this shift was not immediately obvious. However, when cases of opioid misuse (such as heroin) were analyzed, the problem became apparent. While it might seem obvious that needle sharing would link to HIV, in Indiana, while there were cases of hepatitis, HIV was virtually non-existent. With opioid drug use levels climbing only steadily the sudden uplift in HIV cases was peculiar, until data was processed by the Collaborative Advanced Analytics & Data Sharing platform (which was developed by Lockheed Martin).
From this number crunching the trigger point was traced to a small group of opioid drug users in Scott County. The exchange of drugs and needles from this group to other group was the start of the HIV outbreak.
While such analysis is interesting retrospectively, the CDC hope that increased machine learning and stronger computer programs can pick up future trends sufficiently early and allow health experts to attempt to slowdown the spread of future infections.
In related big data and health news, hospitals in Paris are testing out big data analytics and machine learning systems, with a view to devise a system to better forecast admission rates. The aim is to achieve a more efficient deployment of resources and to reach better patient outcomes.
