Bioinformatics, Big Data Analysis, Machine Learning and Artificial Intelligence in Healthcare


The past few years have seen technology advance an almost manic rate. The rapid pace of it acceleration has granted unprecedented developments in various fields of research, especially in biology and medicine. The application of computer science to real-world research has been nothing short of revolutionary in bringing forth results that would not have been possible without digital disruption otherwise. Results from various studies and researches have shed light on outstanding findings and have helped significantly in furthering the improvement of the overall quality of life in society. 

As stated by the Instituto de Salud Carlos III, bioinformatics has been fundamental in the analysis and interpretation of SARS-CoV-2 data. In 2020, the research carried out by the Bioinformatics Unit of the aforementioned centre was essential, since it shed light on such important issues as the sequencing of the genome of the new coronavirus and the automation of diagnostic tests. Bioinformatics research develops and applies informatics and computational tools to improve the management of biological data, by using tools that collect, store, organise, analyse and interpret all these data.

All this would not have been possible without big data. Big data, in simple terms, is massive amounts of information that can work wonders when handled efficiently. It’s become a topic of special interest for the past two decades because of the wellspring of potential hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things. 

In this digital age, information has been key to overall better organisation and new developments. The more information one has, the more optimally we can organize ourselves to deliver the best outcomes. That is why data collection is an important part of every organization. We can also use this data for the prediction of current trends of certain parameters and future events. 

What is the role of big data in all of this?

In this new century, we’re bombarded with copious amounts of data from nearly every aspect of our lives. There is data in everything we do, from the excel sheets you log figures into to your daily sugar levels. It’s become easier to compare different sets of data with each other to a data deluge. 

This has led to the creation of the term ‘big data’ to describe data that is large and unmanageable. In order to meet present and future social needs, the need to develop new strategies to organize this data and derive meaningful information has never been more present – especially when it comes to healthcare. Like almost all industries, healthcare organizations are producing data at a tremendous rate that presents many advantages and challenges at the same time.

Every day, people working with various health organizations around the world are generating a massive amount of data. The term “digital universe” quantitatively defines such massive amounts of data created, replicated, and consumed in a single year. International Data Corporation (IDC) estimated the approximate size of the digital universe in 2005 to be 130 exabytes (EB). The digital universe in 2017 expanded to about 16,000 EB or 16 zettabytes (ZB). IDC predicted that the digital universe would expand to 40,000 EB by the year 2020. To imagine this size, we would have to assign about 5200 gigabytes (GB) of data to all individuals. This exemplifies the phenomenal speed at which the digital universe is expanding. 

Big data has become one of the most prominent key players in fine-tuning certain gaps in the system and identifying weak spots, while also presenting measures to take control of them. 

Real-life applications of big data in healthcare

One of the most significant uses of big data in healthcare is the seamless ability to detect early symptoms of rare diseases through the help of AI in healthcare. This is because thanks to them it is possible to obtain a quick and reliable diagnosis. Rare diseases affect fewer than 5 in 10,000 inhabitants, and according to data from the Spanish Federation for Rare Diseases, FEDER, they affect more than three million inhabitants in Spain. Due to their low prevalence, studies and research are very limited. This is why the emergence of Artificial Intelligence in this area of study has been so important. 

Healthcare is a multi-dimensional system established with the sole aim for the prevention, diagnosis, and treatment of health-related issues or impairments in human beings. However, healthcare has become clogged up with bureaucratic lodgings at every point that it’s become almost impossible for the everyday citizen to get their annual dentist check-up without being bombarded with an overwhelming amount of data to digest. At all these levels, the health professionals are responsible for different kinds of information such as patient’s medical history (diagnosis and prescriptions related data), medical and clinical data (like data from imaging and laboratory examinations), and other private or personal medical data. 

Although bioinformatics has been around for a long time, the 4.0 revolution has been a turning point for all health-related sciences. For some years now, medicine has been using supercomputing to tailor treatments based on the origin of the disease and the patient’s genome. One of the greatest examples of this feat is HIV treatments. With the way things are speeding, it’s going to be no surprise when the universe comes up with another end all, be all cure for the global COVID-19 epidemic.

The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of The World Financial Review.