Tips on Implementation of Machine Learning in The Field of Healthcare

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Machine learning along with a lot of other techniques of data analytics is used in numerous ways in the field of healthcare. Right from image-processing that is used to detect any abnormalities in x-rays and MRI scans, to algorithms which analyzes data in order to detect risk of diseases, progression of diseases, or new diseases, the application of machine learning and data science could substantially improve the processes in the healthcare sector as well as in patient care.

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Tips on Implementation of Machine Learning in The Field of Healthcare

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Machine learning along with a lot of other techniques of data analytics is used in numerous ways in the field of healthcare. Right from image-processing that is used to detect any abnormalities in x-rays and MRI scans, to algorithms which analyzes data in order to detect risk of diseases, progression of diseases, or new diseases, the application of machine learning and data science could substantially improve the processes in the healthcare sector as well as in patient care. However , it is important to remember that such ideas are generally tougher than said to implement in the healthcare sector.

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Here are some things that one must remember when taking  machine learning training  and implementing it in healthcare sector:

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Be Transparent Any diagnosis that is provided to clinicians through algorithms without providing them with any justifications regarding why such an assessment was made could rarely be actionable. It might force the clinicians to do physical exams and do a full chart review in order to determine what the algorithms has picked up on actually . And after all of this, if nothing justifiable has been found, what could clinicians conclude?

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Has the algorithm picking up on something that clinicians are unable to even comprehend, should assessments be judged as unjustified ? There isn’t really any way for clinicians to be fully sure. However, trust in data learning algorithms is highly critical. 

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If the trust is lost, then it might be completely dismissed as it would fail to provide the clinicians with any kind of actionable information . Therefore, if any kind of machine learning model is being used in the healthcare sector by the clinicians, then it is essential that there is transparency offered regarding predictions .   Online data structures and algorithms course  must be provided to the IT department in the healthcare sector.

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Take a More Holistic View In order to be successful, machine learning algorithm must focus more on providing a deeper understanding of how algorithms can be used, processes that it could fit into, and the relationship with the clinicians that are using it. While its very important for such a predictive model to be highly accurate, it is also critical to be aware of ways that it would be used and how algorithms could be used in a more effective manner.

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In order to accomplish that, one needs to consider the areas in the workflow of the clinicians where machine learning algorithms could be implemented, and the value that it would offer to the very goals of clinicians.

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The complete process would consist of lots of backs and forth. Human clinicians make mistakes too, but when it comes to algorithms, the liability would be more visible and profound as well. So , proper care needs to be taken . One needs to learn the basic workflow of the clinicians and the clinicians would be required to take basic  data structures course online  which could help them in many ways and offer them insight into what they are dealing with.

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