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Machine Learning applications in Healthcare May 11, 2017

Posted by 1969mathelc in Machine Learning.
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The technology of Machine Learning has come a long way over the past decade, and it has a very long way to go. Many different applications now revolve around machine learning, and they serve a wide range of purposes. Some of these applications serve as a means of convenience, making everyday life tasks more effortless, or even for entertainment. An example of the use of AI for entertainment is the one in which IBM Watson was used to create a movie trailer for the sci-fi drama Morgan [4]. Other applications of machine learning include much more critical purposes that may involve human lives, or even save them. Due to its potential to save millions of lives in the near future, I believe that machine learning has found a whole new purpose that should be further explored.

In an article from Science Mag, it is said that algorithm has been developed to predict heart at an accuracy that exceeds that of doctors. To be more specific, four different machine-learning algorithms were used, and each scored at an accuracy between 74.5% and 76.4% on approximately 83,000 records compared to 72.8% scored by ACA/AHA guidelines [2]. The algorithms trained themselves on 295,267 records and tested themselves by using available record data from 2005 to predict the outcomes on available data from 2015 [2]. During this analysis, the algorithms were allowed to take into account 22 more data points than the ACC/AHA guidelines, including ethnicity, arthritis and kidney disease [2]. On 83,000 samples, the best algorithm from the four correctly predicted 7.6% more events and raised 1.6% fewer false alarms than the ACC/AHA method.

There is starting to be research revolving around the interpreting DNA with the use of machine learning and AI applications. This is being explored by creating a system that creates molecular effects of genetic variation [3]. The eventual goal of this technology is to allow personal insights to be provided to individuals based on their idiosyncratic biological dispositions [3].

Medical Image Diagnosis is an area where machine learning can excel at. There is currently work being done by Microsoft’s InnerEye, which started as early as 2010, in such area. The InnerEye software is able to recreate scan as a 3D model while pinpointing the extent of and any growth in tumors [1]. Such tool can drastically reduce the time it takes to plan treatment for a patient.

With the proven results we’ve gotten with machine learning and AI, there should no longer be in conversations about the ‘potential’ of the technology, but rather in conversations about how soon the technology should be included in daily activities. The technology has already proven itself of its capabilities, however, doctors’ acceptance of the technology seems to be the biggest burden that needs to be overcome. Doctors, in general, are known to be prideful of their tremendous work, and it is understandable considering the hard work that they put forth and the criticality of their work. Furthermore, just as any other profession, it is not the most pleasant thing to hear that a computer is better at your job than you are. Nevertheless, the goal is not to replace doctors, but rather to assist them. At the end of the day, computers will always be able to process and maintain information at rates that humans cannot. Taking advantage of these processing capabilities would not only make doctors’ jobs easier, but would also have the potential to save millions of lives. It is evident that computers are already being used to process data and help doctors, however the complexity of Machine Learning and Artificial Intelligence takes the scope to a whole new level. The healthcare business is already extremely complicated; it is reasonable that adding a feature, which not many completely understand, can make matters much more complicated. I do not believe that these algorithms should be given the authority to make the final decision on patient cases, or that they should ever have that authority. However, I do believe that it is overdue that these proven technologies should be included in healthcare standards that would allow thousands of lives to be positively affected.

[1] https://www.techemergence.com/machine-learning-healthcare-applications/

[2] http://www.sciencemag.org/news/2017/04/self-taught-artificial-intelligence-beats-doctors-predicting-heart-attacks

[3] https://techcrunch.com/2017/03/16/advances-in-ai-and-ml-are-reshaping-healthcare/

[4] http://www.wired.co.uk/article/ibm-watson-ai-film-trailer

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