The path-breaking advances in technology have found their implementation under various aspects of our day to day lives. One such aspect is Healthcare- our most important need in such fast-paced lives. Indeed, over the course of the late 20th century, life expectancy has increased tremendously, all thanks to revolutionary advances in healthcare systems.
In today’s times, Role Of Machine Learning is at the center of many such innovations that have changed the way we perceive healthcare these days. Whether it’s handling patient data, identifying symptoms and timely diagnosis, or developing new procedures, Machine Learning is at the forefront of a revolution in Healthcare. Role Of Machine Learning has a field of infinite potential, having virtually endless applications in the healthcare industry.
One of the primary aspects that made it all possible was the availability of patient data. We have a tremendous amount of data and systems to use it.
Few of the Role Of Machine Learning are:
1. Predicting Chronic Diseases
Predictions are one of the most important aspects of the healthcare domain. Machine Learning models are being implement up with the help of algorithms like a k-nearest neighbor, decision tree, etc. Deep Learning algorithms with the help of optimizers like RELU(Rectified Linear Activation Function) and Sigmoid activation function along with several optimizers. These techniques applies to predict cancers, heart problems, and even diabetes.
2. Chart patient data and behavior
Electronic Medical Record (EMR) is the format in which patient data is store. However, the data is vast and often several attributes are often ignore by medical practitioners and support staff. In a similar manner, several attribute terms cannot be comprehend by the patient, eventually remaining of no use to the patient. Hence, it’s of extreme importance that the available data is make available to the stakeholder in their usable form.
The user-friendly EMR can be model in terms of an AI-based agent, who can filter the records quickly and display essential information.
3. Ability to pay estimated expenses
thus, Machine Learning algorithms can help us with cost modeling, by showing areas where costs reduce and alternate treatment procedures in place of more expensive ones. In 2018, a research study found that only 17% of patients accounted for almost 75% of healthcare expenditures. however, By addressing their most costly patients, medical experts can have an immediate impact on the total cost of care for individuals.
4. Research in drugs
Researching new drugs and development is a time-consuming process. In order to cut down on research time, drug discovery through high-throughput screening (HTS) of drugs via genomic and transcriptomic data to uncover new relevant drug targets, combined with increasing application of generic algorithms from machine learning.
A fine example is Drug Candidate Identification via molecule docking, in order to predict and preselect interesting drug-target interactions.
Also, Machine Learning offers the ability to test models without standard trials. thereby overcoming the ethical and financial burden of trials on humans.
5. Medical Imaging
Machine Learning is now used along with 3D technology, to precisely map and identify tumors and assist medical practitioners in radiotherapy and surgery. Microsoft’s InnerEye is headlining the advancement.
thus, A fine example of the power of Machine Learning in medical imaging is in identifying Amyotrophic Lateral Sclerosis (ALS). Algorithms can now flag suspecting images and offer risk ratios to convey evidence of ALS. Also, ALS can be distinguished from Primary Lateral Sclerosis (PLS), which is less fatal.
6. Preventing outbreaks of diseases
Machine Learning algorithms are being put to use in today’s times to monitor and predict epidemics around the world. Today, scientists have access to a large amount of data collected from satellites, real-time social media updates, website information, etc. Artificial Neural Networks (ANN) help to collate this information and predict everything from malaria outbreaks to severe chronic infectious diseases.
thus, Predicting these outbreaks is helpful in third-world countries as they lack basic medical support systems and infrastructure and educational systems. A primary example of this is the ProMED-mail, an Internet-based reporting platform that monitors evolving diseases and emerging ones and provides outbreak reports in real-time.
In fact, Blue-Dot, a Canadian startup reported about coronavirus and informed its clients. so, a week before Chinese authorities reported the outbreak in Wuhan and with announcements.
Written by: Viivek Uppalapu
reviewed by: Kothakota Viswanadh
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