Implementing Machine Learning in Health Care

The technology has created cutting-edge solutions in health care facilities. With the inception of artificial intelligence and machine learning technology, the healthcare industry has completely been revolutionized. Artificial Intelligence and machine learning technology has made significant developments in the healthcare field.

The machine learning technology provided with multiple opportunities to hospitals, companies and healthcare facilities to have efficient solutions for Medicare purposes. The machine learning technology helps these facilities to develop models that predict the intensity of the risk patients going through, improving the clinical decisions and care incentives, routine clinical tasks and population health management.

Implementing Machine Learning in Health Care

The machine learning technology is not only effective for theoretical purposes yet also claims its significance at operational side. It is highly effective on multiple chores like cost management, staff management and increasing the patient’s flow. The technology has helped to the organization with practical tools to improve their services, standards of patient’s care, revenue generation and lower chances of risks.

Machine learning technology is highly effective in the terms of increasing the profits of healthcare industry. According to Frost & Sullivan by 2021 research firm, AI systems will generate $6.7 billion in global healthcare industry revenue.

Upon researching further, as per the report of Accenture; machine learning technology is currently increasing the value of healthcare business within three major fields:

  • robot-assisted surgery ($40 billion)
  • virtual nursing assistants ($20 billion)
  • administrative workflow assistance ($18 billion)

Implementing Machine Learning Technology in Healthcare 

    1. Identifying tuberculosis in the developing world:

The advance technology of machine learning is highly efficient and productive in developing countries. The researchers and scientists are currently working on advancing ML technology to X-ray hazardous diseases like Tuberculosis and inspect it intensity. This technology helps in efficiently screen the tuberculosis and treat it.

    2. Detecting brain bleeds:

The machine learning technology is efficient to instantly track the intracranial bleeding after head stroke or trauma in patients. The ML technology has prolong benefits in the hospital emergency rooms to help doctors treat such sensitive cases. Machine learning technology uses deep learning algorithms, patient background history, clinical insights and machinery vision to evaluate the potential loss or cerebral bleeding for physician check-up.

    3. post-traumatic stress disorder (PTSD):

The Tiatros Post Traumatic Growth for Veterans program partnered with IBM Watson to use AI and analytics to ensure more veterans with PTSD would complete psychotherapy. Using these technologies, they achieved a 73 percent completion rate, up from less than 10 percent. As many as 80 percent of veterans with PTSD who finish a treatment program within a year of diagnosis can recover, according to statistics from the Department of Veterans Affairs. Approximately one in five of the 3 million veterans of Afghanistan and Iraq wars suffer from PTSD.

Implementing Machine Learning in Health Care

    4. Detecting Alzheimer’s disease:

Complex and deep learning algorithms have been induced in the ML technology. It helps to diagnose Alzheimer’s disease by examining the speech and voice pattern. The ML technology is advance to provide 82% accurate result by analyzing instantly catching the length of pauses, usage of pronoun over proper noun, extra simple descriptions, the variation in speech amplitude and frequency. The level of accuracy in ML technology is only supposed to be increasing. These are some of the factors that human listeners are unable to grab and attain instantly. Thus, the detection intensity, level of accuracy and analysis are quantifiable via ML technology and much efficient then human.

    5. Cancer diagnosing:

Conventionally the cancers are diagnosed and detected with the help of MRI, CT scan, X-ray and ultrasonography. However, some cancers are dangerous and difficult to diagnose. Thus, even these technologies are not reliable to diagnose cancers and save the precious human life. Other than these, the only technology up till now on which we may rely a bit is by analyzing the microarray gene profiles but it may take hours of computation.

Machine learning technology on the other hand is an effective alternative for diagnosing cancers as well. The latest examples were recently witnessed when 21-board certified dermatologists detected different potential skin cancers with the help of Stanford’s ML technology.

Implementation of machine learning is positively impacting the healthcare industry. Nurses are highly required to research on ML technology and write impeccable nursing essays on such subjects. In case of any lack of research resources or difficulty they may ask us to “Write my Dissertation UK” to write impeccable dissertations.


  Modified On Sep-07-2019 12:21:39 AM

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