Top Applications of Machine Learning in Healthcare

Machine learning is a tool used in health care to assist medical professionals in patient care and clinical data management. It is an artificial intelligence application in which computers are programmed to mimic how humans think and learn. 

 

This can be used in health care to collect and manage patient data, identify healthcare trends, recommend treatments, and more. Hospitals and healthcare organizations have begun to recognize machine learning's ability to improve decision-making and reduce risk in the medical field, which has resulted in a slew of new and exciting career opportunities. 

 

Machine learning in health care is a rapidly evolving field that is more accessible than many folks understand. While the terms ``artificial intelligence" and "machine learning" may appear intimidating at first, many machine learning principles are based on fundamental mathematical and programming skills. 

 

Once you understand the fundamentals of machine learning, you can apply these skills to more advanced concepts and challenges. This can open up new avenues for innovation and diverse career paths in the healthcare field.

 

Introduction to Machine Learning

 

In the real world, we are surrounded by humans who can learn from their experiences, as well as computers or machines that work on our instructions. Can a machine, like a human, learn from past experiences or data? So here comes Machine Learning's role.

 

Machine learning is a data analysis technique that automates the creation of analytical models. It is a subfield of artificial intelligence that is based on the idea that systems can learn from data, identify patterns, and make decisions with little or no human intervention.

 

As the name implies, machine learning is all about machines learning automatically without being explicitly programmed or learning without any direct human intervention. This machine learning process begins with feeding them high-quality data, followed by training the machines by constructing various machine learning models using the data and various algorithms. The algorithms we use are determined by the type of data we have and the task we are attempting to automate.

 

In terms of the formal definition of Machine Learning, we can say that a Machine Learning algorithm learns from experience E about some type of task T, and performance measures P if its performance at tasks in T, as measured by P, improves with experience E.

 

A subset of artificial intelligence is machine learning. It focuses on teaching computers to learn from data and improve with experience rather than explicitly programming them to do so. Algorithms are trained in machine learning to find patterns and correlations in large data sets and to make the best decisions and predictions based on that analysis. Machine learning applications improve with use and become more accurate as they gain access to more data.

 

Machine learning applications can be found in our homes, shopping carts, entertainment media, and healthcare.

 

Also Read | Top 15 Machine Learning Platforms in 2022

 

Machine Learning and Healthcare

 

When it comes to human lives and health, any technology that can provide more efficient, helpful, and faster analysis to provide a proper treatment plan on time is extremely valuable. Artificial Intelligence and its subset Machine Learning are currently sweeping the globe. 

 

Every day, more business use cases appear in technology news. Finance and banking appear to be the best fit for the technology, but what about other industries? Is the healthcare industry unique? Not. When it comes to AI in the medical field, we must recognize the enormous potential and changes that Machine Learning can bring to the healthcare industry. This Google Trends chart depicts the increasing popularity of artificial intelligence in healthcare.

 

One of the most common subsets of Artificial Intelligence is Machine Learning. Its goal is to "train" models with data. According to a Deloitte survey of 1,100 US companies using Artificial Intelligence, 63% were concentrating on Machine Learning. It is a broad technique with potential applications in a variety of industries and uses cases.

 

The application of ML could improve the industry's organizational side. A regular nurse in the United States spends 25% of her work time on regulatory and administrative tasks. These routine tasks, such as claims processing, revenue cycle management, clinical documentation, and records management, could be easily automated by technology.

 

Machine learning is used in a variety of healthcare applications. Machine learning technology, for example, can help healthcare professionals generate precise medicine solutions tailored to individual characteristics by crunching large amounts of data.

 

According to a Mercury News report, machine learning and AI are expected to play a critical role in central nervous system clinical trials in the future.

 

Some machine learning companies are researching how to organize and deliver patient information to doctors during telemedicine sessions, as well as capture information during virtual visits to streamline workflows.

 

Also Read | 11 Real-World Applications of Machine Learning

 

Machine Learning Applications in Healthcare

 

As smart medical devices become more common, technology-enabled healthcare is becoming a reality. Because the healthcare industry welcomes innovation, the future of AI in healthcare looks promising. 

 

Google has already released an algorithm that successfully detects cancer in mammograms, and Stanford University researchers can detect skin cancer using Deep Learning. Artificial intelligence is in charge of processing thousands of different data points, accurately predicting risks and outcomes, and performing a variety of other functions.

 

The growing number of machine learning applications in healthcare enables the healthcare industry to effectively manage data and improve services. Let's take a look at some machine learning applications in the healthcare industry.

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Applications of ML in Healthcare

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1. Medical Data Management

 

Machine learning is advancing the healthcare industry by utilizing cognitive technology to unwind massive amounts of medical records and also to perform any powerful diagnosis. Machine learning can help predict a user's intent. 

 

Implementing machine learning in an organization's workflow can create a personalized user experience, allowing the company to make better decisions and take better actions that benefit the customer and the organization. As a result, machine learning aids in the storage, collection, and reformatting of data.

 

2. Identification and Diagnosis of diseases

 

It is appropriate to begin with this point because ML is very good at diagnosis; in fact, it is one of the most effective areas. Many types of cancer and genetic diseases are difficult to detect; however, ML could handle many of them in their early stages. IBM Watson Genomics is a prime example. 

 

This project combines cognitive computing with genome-based cancer cell sequencing to aid in rapid diagnosis. P1vital's Foretell (Predicting Response to Depression Treatment) is attempting to develop a practical way to use AI to improve diagnosis and treatment in traditional hospitals.

 

3. Disease Detection at an Early Stage

 

Machine learning was crucial in the early detection of medical conditions such as heart attacks and diabetes. Many AI-based wearables are being developed to monitor a person's health and provide warnings when the devices detect anything unusual or unlikely. Fitbit and Apple Watch are two examples. 

 

These devices keep track of a person's heart rate, sleep cycle, breathing rate, activity level, blood pressure, and other vital signs. It records these measurements 24 hours a day, seven days a week.

 

4. Diabetes is predicted

 

Diabetes is one of the most common and deadly diseases. It not only harms a person's health, but it also causes a slew of other serious illnesses. Diabetes primarily affects the kidneys, the heart, and the nerves. Machine Learning could aid in the early detection of diabetes, potentially saving lives. 

 

Classification algorithms such as KNN, Decision Tree, and Bayesian Network could be used to build a diabetes prediction system. In terms of performance and computation time, Naive Bayes is the most efficient.

 

5. Machine Learning in Medicine

 

According to a recent survey, the development of artificial intelligence-based virtual nurses has increased as an engine for medical assistants has grown. According to a recent survey, virtual Nursing Assistants correspond to a maximum of 20 billion US dollars by 2027. A virtual nurse assists in monitoring patients' conditions and providing treatment between doctor visits.

 

6. Machine Learning in Decision Making

 

AI has played a significant role in decision-making not only in the field of health care but also in business by studying customer needs and evaluating any potential risks that a business may face. The use of surgical robots, which can minimize errors and variations, is a powerful use case of artificial intelligence in decision-making.

 

7. Image analysis is used to make Diagnoses

 

Microsoft's InnerEye project is transforming healthcare data analysis. This startup processes medical images using computer vision to make a diagnosis. InnerEye is making more waves in healthcare analytics software as technology advances. Machine Learning will become more efficient very soon, and more data points will be analyzed to make an automated diagnosis.

 

8. Personalized Medicine

 

Machine learning predictive analysis can assist users in receiving personalized treatment. In general, nurses are required to select from a set of diagnoses or predict the patient's risk using a fixed formula based on the patient's history and available genetic information. 

 

Machine learning in medicine, on the other hand, predicts patient data by analyzing medical history to generate multiple treatment options. These treatments are more likely to suit the patient and are more personalized because they are based on the user's data.

 

9. Medical research and Clinical Trial Improvement

 

It's no secret that clinical trials can take years to complete and require significant investments. Based on factors such as a person's history of doctor visits or social media activity, ML can provide predictive analytics to identify the best candidates for clinical trials. The technology will also reduce the number of data-based errors and may recommend the best sample sizes for testing.

 

Conclusion

 

The highest score goes to Machine Learning's powerful abilities in sorting and classifying health data, as well as speeding up doctors' clinical decisions and any types of predictions that can save lives or make surgery less complicated (e.g., the prevention of hypoxemia during surgery). Isn't that a lot already? 

 

Without a doubt, the most valuable thing is human life. Currently, ML in Healthcare provides technologies that directly contribute to the future of advanced medical diagnostics and medicine. Other solutions, such as AI in Nutrition, will be discussed in future blogs.