Tech
5 examples of machine learning and healthcare intersection
2020-08-04 13:47:505 examples of machine learning and healthcare intersection
Both healthcare and machine learning (machine learns and behaves without human interaction) as separate fields are on a rising trajectory. Experts believe that either of these fields are to rise exponentially in the future. But did you know that advancements are being made which lets both of these fields intersect in a way that is unforeseen by the masses. The healthcare industry has always needed training wheels for handling the huge mass of digital data (in other words, Big Data) and machine learning can precisely be the solution. Here, we are going to talk about how an intersection between these two can happen and is happening. Let’s begin on these 5 examples of machine learning and healthcare intersection:
Diagnosis made easy
Machine learning, together with the help of big data is uncovering the types of diagnosis that were never easy with legacy techniques. Machine learning, together with the help of big data is uncovering the types of diagnosis that were never easy with legacy techniques. Predicting is easier with machine learning on healthcare side. This can help doctors’ jobs for catching ailments a lot easier so they can diagnose these properly whenever there is involvement of machine learning. Machine learning has already helped doctors and specialists diagnosis ailments which are hard to diagnose in early stages i.e Cancer.
Matching best healthcare for the patients
With machine learning functionality, healthcare systems will be able to match patients with the best suited healthcare a medical facility can provide. Help like this can at least reduce a year from average mortality time. Therefore, this is saying a big thing about the intersection of both of these technologies
It is reducing costs of handling electronic medical records
Machine learning is supporting healthcare facilities by handling and optimizing electronic medical records. learning
isupporting healthcare facilities by handling and optimizing electronic medical records. Previously, it was a daunting task to deal with. Now the industry has a change when there is involvement of machine learning. Many healthcare platforms are using machine learning to standardize and optimize. It is changing the way these electronic medical records are made. This way they are able to make them easily available across different departme nts of a healthcare facility with seamless integration. The ensuing results are that a platform (which has machine learning functionality) learns to reduce costs by standardizing records across whole healthcare facility.It can identify the best medicine for a particular disease
Machine learning is hard at work to find patterns in data (by looking at behaviour and after-effects). Many healthcare companies are using machine learning this way to find the best medicine for therapy for multi-faceted diseases (high risk of resistance). Therefore, the machine learning algorithm, which they have created is able to find which medicine worked best for a particular disease. This way medicine for any patient wanting treatment becomes more personalized.
Machine learning can save time from avoiding readmission
A lot of time is spent on readmitting a patient. Not only valuable time is a waste but a healthcare facility’s tangible resources are also , vigorously. Healthcare platforms can now use machine learning’s prediction capability to learn which patient is most likely to have a readmission. This way the healthcare facilities are able to reap the benefit of providing an attentive care for the patients. The patients who were most likely to get a readmission. Therefore, this removes the chances of readmission. And, by doing this, medical and financial resources can be saved efficiently.
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Disclaimer: It is of utmost importance that you know all of this well-researched content is for general information. Therefore, you have to keep in mind this does not at all pertains to general treatment. Furthermore, for serious conditions please consult a doctor. For booking appointments with doctors or ordering medicines and lab tests. Please contact My Doctionary.
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