Sentence examples for medical recognition from inspiring English sources

FMT is a breakthrough not in technological or theoretical research, but in medical recognition.

Historically, the elderly female population has received less medical recognition regarding the risk and severity of coronary heart disease (CHD).

Patient 09 (depression on questionnaire prescribed antidepressant) An important implication of describing depression in terms of otherness, either as an illness or a process, was that this made it worthy of medical recognition and intervention.

Thus it will be argued that Armstrong's contention that the Registrar General's creation of a distinct mortality rate for infants both reflected an emerging social awareness of infant mortality, and created social, statistical, and medical recognition of the infant as a separate and important entity, can be extended to foetal mortality.

Armstrong has rightly asserted that the Registrar General's creation of a specific mortality rate for infants, and subsequent subdivision of the first year of life into smaller analysable components, not only reflects the emergence of a social awareness of infant mortality, but indeed created social, statistical, and medical recognition of the infant as a discrete and significant entity.

Historical accounts often attribute the 'rediscovery' of Alzheimer's disease, and its portrayal as the leading cause of a dementia epidemic from the 1960s onwards, to 'government and medical recognition of aging populations and their impending burden on society' (Lock, 2013: 22; Katzman and Bick, 2000).

In the present approach, we employ the WAFs because they have already demonstrated successful performance in medical image recognition, speech recognition, image processing, and other technologies [13 15].

Specifically, we tackled medical entity recognition and relation annotation on texts from MEDLINE, the largest collection of medical literature on the web.

In medical image recognition problems, by using beautiful biologically-inspired architectures, deep learning is able to learn a hierarchical representation of data to distinguish different image classes.

We conclude that deep learning with large scale non-medical image databases may be a good substitute, or addition, to domain specific representations which are yet to be available for general medical image recognition tasks.

Meanwhile, the Metathesaurus is the core database in the UMLS and MetaMap is a medical terminology recognition tool based on the Metathesaurus of UMLS, which is developed by Aronson and Lang [12].