One cannot deny the usage of machines have been significantly increased owing to the breakthrough and its adaptability. In the past half-decade, machines have taken a significant amount of work from humans that resulted in effectiveness and efficiency.
Not only we are able to process work at super-fast speed but also machines have proven to be accurate. This efficiency led the scientist to work on the betterment of machines and they ended up inventing machine learning.
Machine learning (ML) is a process of making machines to adapt and learn by feeding them with a large volume of data. This invention not only helps to work effectively and efficiently but also helps to identify issues and come up with the most appropriate solution to the problem.
In the recent years, data science and machine learning have joined hand and so far, their partnership has proven to be the most effective and efficient and one industry which has embraced their joint venture wholeheartedly is the healthcare sector.
Excitement for superior computing is on the top in the healthcare sector, as providers of the technology have recognized the need of the cutting edge. Professionals like doctors and nurses in the healthcare sector can provide next-level patient care using big data and machine learning algorithms. This partnership has also helped the researcher in the healthcare sector to speed u their work to remain efficient.
So what’s the hurdle in Big Data?
Although, machine learning and data science have joined hands together. But the creation and research of pharmaceuticals were used to be a considerably long process, However, machine learning algorithm and data science have streamlined it in the past few years. Advance and complex modeling and simulations have also allowed researchers flexibility as they believe it gives the researcher to carry out experiments with different biological variables—creating flexibility for them which they generally not required.
Additionally, the data can also be used for pharmacy by adding pharmacists with medication. This will allow pharmacists to review whether medications that patients are already taking is compatible with prescribed medications.
In the current scenario, the technology is able to highlight when medications are taking and prescribed medications are the same. However, this only includes those medications which patient has bought and filled at the local pharmacy. Generally, pharmacists do not know the genetic conditions and health conditions of the patients or other medications that he/she has been taking unless it is disclosed by the patient himself.
Data science is the technology which allows integration of various and unique data to come up with the most valuable output. It includes DNA profiles to medical histories, which helps the doctor and provide them a deeper understanding of the patient and its health.
With the increasing usage of health-related technology, more data is available for analysis than ever before. Going forward, wearable gadgets such as fitness tracker will also help to provide real-time data of the patients—making data available for predictive diagnoses.
MRI, X-rays etc. which are generally described as medical imaging technologies can often have issues regarding their qualities that ultimately impact the dimensions, resolution, and clarity of the resulting image.
However, machine learning algorithms and data science can be used to further improve the technology—resulting in an improvement in image quality, can provide an accurate interpretation along with efficiency. With deep learning, experts believe that machine learning algorithms can have the capacity to improve their accuracy using examples and theoretical knowledge.
So far there are only three examples of machine learning and data science can do the health sector, but experts believe it could increase manifold if proper work is done on the technology. Although we would not welcome a robot surgeon any time soon, pharmacy technology and healthcare sector are changing rapidly. The partnership of machine learning and data science has allowed us to build healthier tomorrow for coming generations.