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Predictive Analysis in Healthcare May 9, 2017

Posted by francinearchie82 in Machine Learning.
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Predictive analysis is a growing trend that deserves all the hype it receives. Important decisions can be made based upon undiscovered patterns that exist within data sets by leveraging predictive analysis decision support. In healthcare, machine learning could benefit the area of predictive analysis by helping with managing the surrounding population of healthcare organizations.

Consider an 18-year-old patient that has electronic health records for the duration of their life; now multiply that by all 18-year-old patients that have electronic health records in a single healthcare organization, and you can imagine how you could have a nice amount of data to review. From birth to 18, you can track, in addition to age, patient attributes such as weight height, demographics like address/location/area and salary, frequencies of visits to a primary care physician, proximity to the healthcare facility, and medical conditions like high blood pressure and diabetes. With a data set that rich, predictive analysis can begin to predict not only patient outcomes, such as medical conditions that present with frequent item sets like high weight and low primary care physician visits, but also, how many patients with that confidence of the above union, annually visit their primary care physician, and if there is commonality, begin a study to promote annual visits to the doctor, and possibly build a clinic in areas of concentrated need. A study like this is a way to collectively manage the patient population to ensure healthy populations, but also to be proactive about healthcare versus treating a patient in facilities or departments that could be avoided like the emergency room or the urgent care.

Algorithms can teach and build models to aide in making intelligent decisions without human intervention. Decision support would have a valid impact from rich data, and healthcare can then become personalized. There are currently many real world activities being done from clinical genomic analysis to neuroscience, to drug resistance tuberculosis, and they will all contribute to the growing field of population health and managed care. On the radar also are the projections that could be coming, such as health insurance agencies billing based on patient attributes, using data analysis form predictive analysis.

Source:

“Cognitive Analytics and Solutions.” IBM. https://www.research.ibm.com/haifa/dept/vst/mldm.shtml

“Machine Learning and Health Care mean $6M for Prdilytics.” Harris, Derrick. 5 Sept. 2012. Gigaom. https://www.research.ibm.com/haifa/dept/vst/mldm.shtml

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