AIMC Topic: Language

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The neural architecture of language: Integrative modeling converges on predictive processing.

Proceedings of the National Academy of Sciences of the United States of America
The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models,...

Preliminary Text Analysis from Medical Records for TB Diagnosis Support.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Tuberculosis is an infectious disease that is spread through the air from one person to another and is one of the top ten causes of death in the world according to the World Health Organization. From biomedical engineering, decision support systems b...

Analysis of Language Embeddings for Classification of Unstructured Pathology Reports.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A pathology report is one of the most significant medical documents providing interpretive insights into the visual appearance of the patient's biopsy sample. In digital pathology, high-resolution images of tissue samples are stored along with pathol...

Predicting Severity in People with Aphasia: A Natural Language Processing and Machine Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Speech language pathologists need an accurate assessment of the severity of people with aphasia (PWA) to design and provide the best course of therapy. Currently, severity is evaluated manually by an increasingly scarce pool of experienced and well-t...

Comparison of ACM and CLAMP for Entity Extraction in Clinical Notes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Rapid increase in adoption of electronic health records in health care institutions has motivated the use of entity extraction tools to extract meaningful information from clinical notes with unstructured and narrative style. This paper investigates ...

Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.

The Journal of the Acoustical Society of America
The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed to assess the ability to generalize ...

MT-clinical BERT: scaling clinical information extraction with multitask learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Clinical notes contain an abundance of important, but not-readily accessible, information about patients. Systems that automatically extract this information rely on large amounts of training data of which there exists limited resources to...

Biomedical and clinical English model packages for the Stanza Python NLP library.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The study sought to develop and evaluate neural natural language processing (NLP) packages for the syntactic analysis and named entity recognition of biomedical and clinical English text.

Deep-learning-based automated terminology mapping in OMOP-CDM.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such sch...

A span-graph neural model for overlapping entity relation extraction in biomedical texts.

Bioinformatics (Oxford, England)
MOTIVATION: Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing. Compared with traditional pipeline methods, joint methods can avoid the error pr...