• Used majorly in research-based applications for testing, trying new drugs, and collating medical records, an extrapolation of these applications is proof of how big data saves lives in unconventional medical ways.
We have heard of technology simplifying our tasks, reducing the hard work, and improving efficiency. Advancements in medical sciences have helped us discover and invent new medication for diseases that were once thought of as incurable. A new trend has been added to this application of saving lives with technology. Big data, the field of technology that deals with massive amounts of data, is being applied to create life-saving solutions for problems related to human medical requirements. A majority of the research carried out today is proof of how big data saves lives.
Big data saves lives in 3 ways
Medical research carried out in any part of the world needs testing and trying to learn about the possible side-effects of a new drug or pill. The data generated from such tests is massive and requires supercomputers programmed with regression and clustering models to detect patterns and deduce results from a set of un-clustered data.
1. Studying new medication
As mentioned above, a new drug or pill needs to undergo extreme testing to be verified before being introduced into the market. These tests are carried out on a diverse set of people from different backgrounds and medical conditions. The test data generated includes multiple parameters like age group, racial backgrounds, medical conditions, and even genetic patterns. With big data models developed to treat cluttered, raw data like this, all these test results can be supplied to these models. They apply relevant data processing methods to generate organized results that take into consideration every parameter and weigh the results against them. This offers a comprehensive view of how the proposed drug would work, allowing for any change in its composition if required.
2. Testing drug-drug interactions
All drugs formulated undergo rigorous testing before making them available for public use. However, every drug needs to be tested with and against each other to ensure compatibility when consumed together. Various big data statistical models help in simplifying these test results, to detect unhealthy drug combinations that can be unsafe. One such example of employing data mining for detecting abnormalities in medicine is the research carried out by the Stanford University where they detected that Praxil, an anti-depressant, and Pravachol, a cholesterol-lowering drug, were fatal for diabetic people when consumed together.
3. Bypassing clinical trials
A test group for any drug trial can be hard to configure, especially if a generic drug is on trial. There have been many cases where a drug worked with one set of people but proved dangerous for another set. This discrepancy in clinical trials can be taken care of if the patient data records from various hospitals are collected together. The drug can be then tested against any previous drug similar to it with an extensive database of parameters to weigh against. This application is being currently used to study the links between statin drugs and potential to lower prostate cancer risks, especially in patients.
Future of big data in medicines
The big switch from not using big data in medicine to wholly using big data in medicine is not a quick process. The technology, no matter how slow but steady, is not just limited to research-based applications. Big data models can be used to acquire better staffing, predicting epidemic outbursts, offering telemedicine, and carrying out general conditional checks on hospitals to determine hospital-based infections. This is crucial for developing better medication and offering improved healthcare services to people.
