The Impact of Machine Learning on Biomedicine
Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Advancements in machine learning have made our world and lives much better by impacting almost every aspect of our lives. We are now living in an era where technology is influencing the way medical facilities and medicine are administered to people but this requires a lot of research and development.
As the technology improves and there is more research into applications of machine learning, it will know no bounds in improving our lives and predicting anything. As as right now, the main applications of machine learning within the field of biomedicine are:
Identification of Diseases and Illnesses
Many large pharma companies are making use of machine learning methodologies that are enhancing focus on better assessment of data and identification markers. Many biomedical organizations have also been testing and implementing programs to help identify certain diseases, illnesses and possible complications through the use of biomarkers and physical conditions. Some of these programs have already been implemented in public hospitals and clinics to help prevent possible problems.
Personalized Treatment
Personalized medicine is a more effective treatment based on individual data paired with predictive analytics. This technology will analyze a patient’s personal information and history in order to optimize the treatment option. Although this research is at the initial stage, it holds a lot of prospects in the future. With the use of data about patients, it will be easier for medical practitioners to render the right kind of medicine to individuals.
Drug Discovery
The use of machine learning in drug discovery is still in its early stages but once fully developed, it will impact the lives of almost everyone. It theorizes that the initial screening of drugs will predict the success rate of medicine based on the patient’s personal medical information. It’s also experimented with the compatibility of sequencing between a drug and someone's genes by tracking the impact it’s had on previous patients. Ultimately, these programs should be able to provide the best option to patients but also effective alternative therapy treatments.
Clinical Trial Research
Using predictive analytics to identify candidates for clinical trials can draw a much wider range of data than the technologies we are using today and more effectively make conclusions. They will draw on information including genetic information, doctor’s visit, etc to help select a better sample pool.
Machine learning within the field of biomedicine is still in its early stages with most of its projects still being tested or in its beta stages. As time goes on, many companies will be able to collect more data thus making this technology more accurate and effective. Machine learning has triggered the medical revolution by improving it and making it more efficient and flawless.
