Imagine going to the doctor’s when you are not feeling well. And, instead of the doctor only asking you how you feel and then coming up with a diagnosis based on acquired medical knowledge, he/she does something more – enters your symptoms into a computer that retrieves the most recent and related medical research in seconds. Additionally, he/she goes ahead to review your medical records, family history and compares that with the acquired research to obtain a correct diagnosis and the right treatment procedures for your problem.
Well, over the past few years, technological advancements have evolved significantly and have in turn revolutionized many industries including the healthcare industry. Medical diagnosis is no longer just conventional and standard. With emerging technologies such as big data, AI, and machine learning, patients can now receive specialized and tailored precision medicine.
Understanding Machine Learning and Big Data
Big data and machine learning have become hot buzzwords not only in tech companies but in other contemporary organizations. Big data refers to huge chunks of structured and unstructured data that is from both traditional and digital sources within an organization and represents the source for ongoing analysis. Machine learning, on the other hand, is the ability of a computer to analyze big data, extract information, and learn from it.
Machine learning is closely related to Artificial Intelligence (AI) – a technology that enables computers to mimic human intelligence and enable them to carry out tasks in a smart way. The purpose of machine learning is to achieve as many positive outcomes with increasingly precise predictions. Both big data and machine learning technologies can be implemented in computers to enable smart intelligent decisions.
For instance, two years ago, a computer program developed by Google outsmarted the best human player in a machine versus human ‘Go’ game challenge. Go is a Chinese-based board game with more possible moves and options than there are atoms in the universe. As such, it is extremely difficult to compute the moves that players in the game might make. However, by learning the moves from experience, mimicking human brain intelligence as well as neural networks the computer was eventually able to beat the best game’s grandmaster.
Many organizations now realize that big data can be processed and analyzed for insights never discovered before. The health industry itself has not been left behind.
How Big Data and Machine Learning are Transforming Healthcare
From advances in tailored doctor appointments to neurorobotics, technology is reshaping the healthcare sector. Like other industries, the health sector has huge data volumes from health records, genomic data, health scans, smartphone apps, and wearables. As a result, healthcare organizations are progressively finding ways to constantly produce data-driven insights through the implementation of machine learning which delivers solutions for processing data, developing and deploying algorithms to generate insights to help solve ongoing healthcare challenges.
Here are some functions transformed by machine learning and big data in the health sector:
- Medical Checkups and Diagnosis
In the UK alone, approximately 200 go blind every day as a result of age-related macular degeneration (AMD) – a treatable condition if detected on time. Machine learning can identify patients likely to suffer AMD, who can then be treated and their sight saved.
Computer programs can ‘learn’ crucial features of a big data to make accurate predictions that were not previously known to detect or diagnose disease. For example, machine learning can process and analyze medical images in seconds and capture information that MRI scans cannot. Machine learning algorithms, therefore, have the ability to predict the likelihood of malignancy or mortality from a disease. Additionally, there are now innovative contact lenses with the capability to discover the glucose levels of diabetics.
By teaching machines to learn human intuition, it is now possible to catch and detect problems that the human eye may not otherwise catch during checkups. Machine-learning apps that can help detect when people are getting near depressed or when a bipolar disorder is on the verge of occurring in a way that no psychiatrists ever could are now available. With these machine learning algorithms, computers are getting more and more adroit at identifying patterns which is pretty much what diagnostics are all about.
- Enhanced Medical Treatment
Adding to the fact that computers can accurately and quickly make the correct diagnosis when fed with big data and machine learning techniques, they can also come up with better and tailor-made treatments that can save more lives, time and money.
Innovations in auto diagnostics, smart pills, genome sequencing, body parts 3D printing, and implantable drug deliveries are transforming the way healthcare is provided. Patients now have access to treatments that are beyond standard procedures. For example, IBM’s Watson – a supercomputer – is helping cancer patients who have exhausted standard treatments with specialized treatments. Such treatments are now based on a careful analysis of a patient’s specific condition without being very general.
In addition, robotics surgeries are also enhancing treatment processes. And, although not all robotic surgery procedures have to do with machine learning, some combine computer vision and machine learning to identify specific body parts and distances such as identifying hair follicles for a hair transplantation surgery. The da Vinci robot is in the limelight in the robotic surgery space. It allows surgeons to manipulate robotic limbs in order to perform surgeries with finer detail than would be possible by the human hand alone.
- Drug Discovery and Development
Another area where big data and machine learning are dramatically impacting in the health industry is in drug discovery.
In 2016, the global drug discovery which is involved in the discovery and drugs design through laboratory testing to treat diseases and infections was placed around 35 billion dollars. Its value is now estimated to grow to approximately 71 billion by 2025. Although increasing in growth, the process is time-consuming requiring a lot of research data and capital.
Drug discovery and development enterprises are therefore continuously seeking ways to reduce incurred costs and increase efficiency which is where big data and machine learning comes in. With an influx of big data from research and development centers all over the world, patients, social media, retailers as well as caregivers, the lengthy drug discovery process can be reduced and better drugs released for pharmaceuticals more quickly. Pharmaceutical companies also are deliberating on how to use big data to improve clinical trials. Big data enhances clinical trials by handling huge data sets and processing it much faster and more efficiently
- Treatment Schedules and Follow-up Care
Big data in healthcare is being used to predict better ways to handle patient calls, booking, messaging, records, documents, imaging, invoicing and many more. It is also helping in discovering factors that speed patient recovery as well as determining and lessening time taken by patients in the hospital after a treatment. Reducing the time a patient takes to recover help lowers hospital expenses and hints on ways to improve patient care. By finding ways on how to lessen time taken by patients in hospitals, healthcare institutions are able to admit and make treatment available to more patients.
Well, machine learning algorithms can help generate those kinds of insights. For instance, the technology can be used to predict patients stay in hospitals by analyzing factors such as patient’s age. Such predictions can be used as the basis for future healthcare outcomes and help identify patients who are likely to experience difficulties in the recovery process. The predictions can also be extended to make better clinical decisions and lower patient admission fees.
More than people, machines can interpret data better than the human mind. And while computers and robots will probably never replace doctors and nurses, they are no doubt changing the way health institutions are providing healthcare.
With the ability to predict what and how aggressive a person’s condition might be, and to know which treatments may or may not work well, machine learning has become an integral and ultimately indispensable part of medical care. It is a high time to realize that it’s not a competition between machines and the human mind, but that both man and machine can finally come up with significant improvements in healthcare.