What I like about working on machine learning here at Elsevier is that we can combine theory with practice..— Georgios Tsatsaronis, Principal NLP Specialist
Technology Research Director
The challenge with automating healthcare technology is that the stakes are very high, since lives are on the line.
One of my projects was doing image classification for ClinicalKey, allowing users to search for both images and text – a game changer for medical professionals. The challenge with automating healthcare technology is that the stakes are very high, since lives are on the line. Therefore, we have to reach accuracy levels of 90 percent and higher. At the same time, in healthcare you also get the biggest bang for your buck with machine learning. For example, computers are now as good or even slightly better as humans in predicting diabetic retinopathy (diabetes induced blindness) by looking at pictures of the retina. More and more, machines will fulfil this kind of service role. This does not mean that computers will replace doctors, though. It simply helps them save time on routine jobs, which they can then spend on the things they are best at, be it a niche specialization, diagnosing a rare illness, or simply engaging with their patients.
Sujit Pal is Technology Research Director in the Labs group, where he focuses on search, natural language processing, machine learning and distributed processing.
More on Machine Learning from our tech employees
If machine learning could improve the quality of science, this would have huge implications on the price and speed of drug manufacturing..— Helena Deus, Senior Technology Researcher
The data that we have at Elsevier is a goldmine of information for a lot of interesting and intelligent things..— Deep Kayal, Machine Learning Engineer