Machine Learning in Healthcare – Unlocking the Full Potential!

Netflix, Siri, and websites that recommend items based on other people’s purchase behavior. What do these have in common? These are real-world examples of machine learning being used. Machine learning is the process of teaching machines to recognize patterns by providing them data and an algorithm to work with the data. And it has helped a lot in the field of healthcare in a number of different ways.

Many sectors are using machine learning, healthcare cannot stand behind! Google has developed an ML algorithm to identify cancerous tumors, Stanford is using it to identify skin cancer.

Experts call the process of machine learning as ‘training’ of machines and the output that is produced is known as ‘model’. The model is provided with data and it creates new information with whatever it had previously learned.

The three types of models used in machine learning are:

machine learning models - machine learning in healthcare

  • Classification – The purpose of this model is to determine a category- it is one thing or another. The model is trained to categorize the dataset.

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  • Clustering – This model is created when there is a bunch of data available but didn’t have a determined outcome and just want to see distinctive patterns in the data.
  • Regression – This model is created for the purpose of finding value. With the help of data, the algorithm can find associations between any two variables and the outcome is predicted accordingly.

“Computers are able to see, hear and learn. Welcome to the future.”

– Dave Waters

Machine Learning in Healthcare

machine learning in healthcare - Benefits & Applications

Let’s quickly explore the advanced machine learning applications in healthcare sector:

1. Identification of Diseases and Diagnosis

It is hard to diagnose diseases manually, machine learning plays a huge role in identifying the patient’s disease, monitor his health, and suggest necessary steps to be taken in order to prevent it. It can include anything from minor diseases to major ones such as cancer which is tough to identify in the early stages.

Any type of cancer is a killer disease and researchers are fighting every day to get new solutions and developments to help the people.

For example – QuantX is powered by machine learning and artificial intelligence and developed in the labs of the University of Chicago. It addresses the critical needs of patients and practice administrators and provides information that enables faster and more accurate diagnosis, individualized treatment, and improved outcomes. The goal of this development was to provide better results and improved diagnosis by radiologists for the patients.

2. Drug Discovery and Manufacturing

R&D technologies such as next-generation sequencing and precision medicine can help to find therapy of multifaceted health diseases. Machine learning algorithms such as unsupervised learning can identify patterns in data without providing for any predictions.

Discovering or manufacturing a new drug can be expensive and a long process because there are a number of compounds that are put to the test and only one result can prove to be useful. With the advancements in technology, machine learning can lead to stimulating this process.

For example – Insitro, a California based startup is aimed at discovering new drugs and medicines to cure more patients, sooner, at a lower cost. It has combined technologies like data science, machine learning, and other advanced laboratory technologies to observe and construct biological model systems and solve problems that they previously couldn’t. It is even partnering up with other companies to provide better results.

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Another example is the Project Hanover by Microsoft which is also using machine learning-based technologies for initiatives such as developing technologies for cancer treatment and personalizing drug combinations for patients.

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3. Medical Imaging

With the help of machine learning techniques such as deep learning, it is now possible to find microscopic deformities in the scanned images within the patients and as a result, doctors are able to suggest a proper diagnosis. Traditionally, techniques like x-ray and CT scan were enough to inspect minor irregularities, but with the increasing diseases, there was a need to inspect them properly.

For example – Machine learning and Deep learning are both responsible for the advancement of technology known as Computer Vision. This technology was used in Microsoft’s InnerEye project which works for making tools for image analysis. InnerEye is a research project that uses machine learning technology to build innovative tools for the automatic, quantitative analysis of 3D radiological images. This project employed machine learning to differentiate between tumors and healthy anatomy using these images. This helped to assist the experts in the field of radiotherapy and surgical planning.

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4. Personalized Medicine/Treatment

With the explosion of patient data in the form of genetic information and electronic health records, doctors are able to provide personalized treatment to individual patients according to their precise needs. Their aim is to gain insights from massive amounts of datasets and use it to make patients healthy at individual level. These insights are able to suggest personalized combinations, and predict disease risk with the help of machine learning technologies.

For example – Watson healthcare is one of IBM Watson’s projects that include the usage of machine learning. This is creating powerful resources for improving the health of the patient. Watson healthcare decreased the time of doctors that was spent on making treatment decisions by providing them with individualized treatment options based on the analysis of recent research, clinical practices, and trials. This machine learning-based project can now offer treatment for blood cancer and many more diseases. Such renowned cancer expertise gives every patient access to the best possible decision treatment.

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5. Smart Health Records

While technology has eased the process of data entry, there are still some processes that take up a lot of time. Maintaining up-to-date health records every day is exhausting as well as time-consuming. After initiating such huge works, maintaining health records is another area where machine learning has entered to save time, effort, and money. Google’s Cloud Vision API and MATLAB’s machine learning-based handwriting recognition technology are used for document classification methods.

For example – Ciox, a European health technology company, uses machine learning technologies to enhance the management of health information and health information exchange. The goal is to facilitate access to clinical data, modernize the workflow in the company, and improve the accuracy of health information. Ciox health also developed smart charts that are utilized to identify and extract health data from various medical records to aggregate a patient’s medical history into one digital profile.

6. Predicting Diseases

Various machine learning technologies are being put to use in monitoring and predicting outbreaks around the world. Scientists have access to a massive amount of data collected from satellites, social media platforms, websites, etc. ML techniques such as artificial neural networks help to collaborate with this information and predict everything from minor diseases to severe chronic infectious diseases.

For example – Researchers at the University of Nottingham in the UK created a system with the help of machine learning and artificial intelligence that scanned patient’s medical data and predicted which of the patients would have heart attacks within 10 years. When compared to the traditional prediction system, ML and AI systems correctly predicted the chances of a heart attack in more than 300 patients.

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Summary

People need to stop considering machine learning as a concept from the future and instead, embrace the tools and opportunities it is making available for us. These applications of machine learning are advancing the field of medicine into a completely new domain which makes it exciting to think about where it can go in the future.

Any queries in the Benefits of Machine Learning in Healthcare? Share your views in the comments.

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