Real-World Applications Of Machine Learning In Healthcare

The healthcare industry has always benefited from technological advances and their offerings. From pacemakers and X-Rays to electronic CPRs and more, healthcare has been able to add value to society and its evolution immensely due to the role of technology. Taking the evolution forward at this phase of advancements is Artificial Intelligence (AI) and its allied technologies such as machine learning, deep learning, NLP, and more.

In more ways than imaginable, AI and machine learning concepts are helping doctors and surgeons save precious lives seamlessly, detect diseases and concerns even before their advent, manage patients better, engage more effectively in their recovery process, and more. Through AI-driven solutions and machine learning models, organizations around the world are able to better deliver healthcare to people.

But how exactly are these two technologies empowering hospitals and healthcare providers? What are the real-world tangible applications of use cases that make them inevitable? Well, let’s find out.

The Role Of Machine Learning In Healthcare

For the uninitiated, machine learning is a subset of AI that allows machines to autonomously learn concepts, process data, and deliver desired results. Through different learning techniques such as unsupervised, supervised learning, and more, machine learning models learn to process data through conditions and clauses and arrive at outcomes. This makes them ideal to churn out prescriptive and predictive insights.

The Role Of Machine Learning In Healthcare

These insights immensely help in the organizational and administrative side of healthcare delivery such as patient and bed management, remote monitoring, appointment management, duty rosters creation, and more. On a daily basis, healthcare professionals spend 25% of their time on redundant tasks such as records management & updation and claims processing, which prevents them from delivering healthcare as required.

The implementation of machine learning models could bring in automation and eliminate human intervention in places they are least required. Besides, machine learning also helps in optimizing patient engagement and recovery by sending out timely alerts and notifications to patients on their medications, appointments, reports collection, and more.

Besides these administrative benefits, there are other practical benefits of machine learning in healthcare. Let’s explore what they are.

Real-World Applications of Machine Learning

Disease Detection & Efficient Diagnosis

One of the major use cases of machine learning in healthcare lies in the early detection and efficient diagnosis of diseases. Concerns such as hereditary and genetic disorders and certain types of cancers are hard to identify in the early stages but with well-trained machine learning solutions, they can be precisely detected.

Such models undergo years of training from computer vision and other datasets. They are trained to spot even the slightest of anomalies in the human body or an organ to trigger a notification for further analysis. A good example of this use case is IBM Watson Genomic, whose genome-driven sequencing model powered by cognitive computing allows for faster and more effective ways to diagnose concerns.

Efficient Management of Health Records

Despite advancements, the maintenance of electronic health records is still a plaguing concern in the healthcare sector. While it is true that it has become a lot easier compared to what we collectively used earlier, health data is still all over the place.

This is quite ironic because health records need to be centralized and streamlined (let’s not forget interoperable, too). However, a lot of crucial details that go missing from records, are either locked or wrong. However, the influence of machine learning is changing all these as projects from MathWorks and Google are helping in the automatic updation of even offline records through handwriting detection technologies. This ensures healthcare professionals across verticals have timely access to patient data to do their job.

Diabetes Detection

The problem with a disease like diabetes is that a lot of people have it for a prolonged period of time without experiencing any symptoms. So, when they actually experience the symptoms and effects of diabetes for the first time, it’s already quite late. However, instances like these could be prevented through machine learning models.

A system built on algorithms such as Naive Bayes, KNN, Decision Tree, and more could be used to process health data and predict the onset of diabetes through details from an individual’s age, lifestyle choices, diet, weight, and other crucial details. The same algorithms could also be used to detect liver diseases accurately.

Behavioral Modification

Healthcare is beyond treating diseases and illnesses. It’s about overall wellbeing. Often, we as humans reveal more about ourselves and what we go through with our bodily gestures, postures, and overall behavior. Machine learning-driven models can now help us identify such subconscious and involuntary actions and make necessary lifestyle changes. This could be as simple as wearables that recommend you to move your body after prolonged periods of idle time or apps that ask you to correct your body postures.

Discovering New Drugs & Medications

‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ ‘ Discovering New Drugs &Amp; Medications

A lot of major health ailments still don’t have a cure. While there are immediately life-threatening concerns like cancer and AIDS on one side, there are also chronic illnesses that could eat up individuals for their entire life such as autoimmune diseases and neurological disorders.

Machine learning is immensely helping organizations and drug manufacturers to come up with medications for major diseases faster and more effectively. Through simulated clinical trials, sequencing, and pattern detection, companies are now able to fast-track their experimentation and observation processes. A lot of unconventional therapies and remedies are also being developed in parallel to mainstream medicine with the help of machine learning.

Wrapping Up

Machine learning is significantly reducing the time required for us humans to reach the next phase of evolution. We are now moving ahead at a pace faster than how we got here. With more use cases, experiments and applications, we could be discussing how cancer has been cured or how a devastating pandemic avoided due to a simple smartphone app in the coming years. AI in Healthcare is revolutionizing the medical industry.

Originally published here

Real-World Applications Of Machine Learning In Healthcare