Data science and machine learning are now being used in every sector. Earlier we have talked about big data applications in healthcare and the importance of data science in education.
In this post, we will be talking about machine learning applications in healthcare. If you’re a student of machine learning, you can use these applications and try to implement them using machine learning algorithms. For some of these applications, research work is going on and even have been implemented. While for others, initial work is in progress and we can expect to get in real-life soon.
5 Machine learning applications in healthcare
Let’s start and look for the top 5 machine learning applications in healthcare that can be implemented to make the healthcare system better.
#1 Identify disease and diagnosis
One of the major applications of machine learning in healthcare is to identify potential disease and diagnosis. This can be used to identify any kind of disease starting from cancer to any normal disease. Again for a disease like cancer, it can also detect the stage of cancer whether it is at the critical stage or initial stage. Machine learning algorithms like logistic regression or random forest, or others. The best thing about such prediction is, it can be cured in a timely manner and the casualty rate can be decreased.
#2 Drug discovery and manufacturing
Machine learning can also be used in early-stage drug discovery and then in it’s manufacturing. This includes sequencing and finding the pattern in medicine. For such pattern detection, unsupervised machine learning can be used.
#3 Handling administrative tasks using NLP
One of the huge burdens of researchers is the administrative task and during this process, they spend lots of time. If these can be somehow automated then they can focus on the work where they are best at.
This admin task majorly involves reviewing and updating the electronic health record of the patients. And nearly every hospital in the united states is using the same way to maintain electronic health records. Here, in that case, NLP can be used to identify and categorize words and phrases. This way, the doctor need not handle lots of tasks and they can just review the categorization which will be an easy way to work for them.
#4 Risk Identification
Healthcare vendors can create multiple tools on top of machine learning that can use anomaly detection to predict such as stroke, heart attack, and other complications. These tools can be trained based on the previous data, their health records, daily health evaluations, real-time measurement, etc. Further, the tool can alert the hospital staff in case it detects any risk in the patient.
#5 Medical imaging diagnostics
Machine learning and deep learning take us to an interesting topic called computer vision. Many organizations are working to develop the tools that provide diagnostic for image analysis for practionatior.
Nowadays, there is a various medical imagery data source which makes this easy to create such a system.
Example- One of the best examples for the same can be the analysis of skin images to identify skin cancer. In any case, these tools have given great results and even surpassed manual detection as well. In the past, these tools have shown an accuracy of 87-95% whereas dermatologists have an accuracy of 65-85% in detecting melanomas.
These were the top 5 machine learning applications in healthcare. If you’re a machine learning engineer and looking to work in the healthcare sector, you can start with these examples. Look out for the free datasets and start developing any such tools.
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