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Transfer Learning from BERT to Support Insertion of New Concepts into SNOMED CT.

AMIA ... Annual Symposium proceedings. AMIA Symposium
With advances in Machine Learning (ML), neural network-based methods, such as Convolutional/Recurrent Neural Networks, have been proposed to assist terminology curators in the development and maintenance of terminologies. Bidirectional Encoder Repres...

Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide.

Research synthesis methods
Machine learning (ML) algorithms have proven highly accurate for identifying Randomized Controlled Trials (RCTs) but are not used much in practice, in part because the best way to make use of the technology in a typical workflow is unclear. In this w...

Measuring the effect of different types of unsupervised word representations on Medical Named Entity Recognition.

International journal of medical informatics
BACKGROUND: This work deals with Natural Language Processing applied to the clinical domain. Specifically, the work deals with a Medical Entity Recognition (MER) on Electronic Health Records (EHRs). Developing a MER system entailed heavy data preproc...

Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developin...

Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE.

Systematic reviews
BACKGROUND: Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieva...