Now showing items 1-6 of 6
Automatic knowledge extraction from EHRs
(2016-07-10) - Conference item,
Increasing efforts in the collection, standardization, and maintenance of large scale longitudinal elec- tronic health care records (EHRs) across the world provide a promising source of real world medical data with the ...
Learnt quasi-transitive similarity for retrieval from large collections of faces
(IEEE Computer Society, 2016-06-26) - Conference item,
We are interested in identity-based retrieval of face sets from large unlabelled collections acquired in uncontrolled environments. Given a baseline algorithm for measuring the similarity of two face sets, the meta-algorithm ...
Weighted linear fusion of multimodal data - a reasonable baseline?
(Association for Computing Machinery, Inc, 2016-10-01) - Conference item,
The ever-increasing demand for reliable inference capable of handling unpredictable challenges of practical application in the real world, has made research on information fusion of major importance. There are few fields ...
Towards sophisticated learning from EHRs : increasing prediction specificity and accuracy using clinically meaningful risk criteria
(IEEE, 2016-08-16) - Conference item,
Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of ...
Analysing the history of autism spectrum disorder using topic models
(IEEE, 2016-10-17) - Conference item,
We describe a novel framework for the discovery of underlying topics of a longitudinal collection of scholarly data,and the tracking of their lifetime and popularity over time. Unlike the social media or news data, as the ...
Fairer citation based metrics
(2016-09) - Journal article,
I describe a simple modification which can be applied to any citation count based index (e.g. Hirsch’s h-index) quantifying a researcher’s publication output. The key idea behind the proposed approach is that the merit for ...