AIMC Topic: Natural Language Processing

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Neural Encoding and Decoding With Distributed Sentence Representations.

IEEE transactions on neural networks and learning systems
Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, ha...

A Survey of the Usages of Deep Learning for Natural Language Processing.

IEEE transactions on neural networks and learning systems
Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learning archite...

Latent Dirichlet allocation model for world trade analysis.

PloS one
International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be co...

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer.

International journal of radiation oncology, biology, physics
Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language p...

A Method for Generating Synthetic Electronic Medical Record Text.

IEEE/ACM transactions on computational biology and bioinformatics
Machine learning (ML) and Natural Language Processing (NLP) have achieved remarkable success in many fields and have brought new opportunities and high expectation in the analyses of medical data, of which the most common type is the massive free-tex...

Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.

Journal of psychiatric research
Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting "current" suicidal ideation from uns...

The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media.

PloS one
We examine gender bias in media by tallying the number of men and women quoted in news text, using the Gender Gap Tracker, a software system we developed specifically for this purpose. The Gender Gap Tracker downloads and analyzes the online daily pu...

CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and pati...

Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. ...

Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping Review.

Journal of medical Internet research
BACKGROUND: Interoperability and secondary use of data is a challenge in health care. Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) has become the uni...