AI Medical Compendium Topic

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Information Storage and Retrieval

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An Approximate GEMM Unit for Energy-Efficient Object Detection.

Sensors (Basel, Switzerland)
Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-...

Improving the recall of biomedical named entity recognition with label re-correction and knowledge distillation.

BMC bioinformatics
BACKGROUND: Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them.

A Sentence-Level Joint Relation Classification Model Based on Reinforcement Learning.

Computational intelligence and neuroscience
Relation classification is an important semantic processing task in the field of natural language processing (NLP). Data sources generally adopt remote monitoring strategies to automatically generate large-scale training data, which inevitably causes...

Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.

PloS one
Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza a...

Med7: A transferable clinical natural language processing model for electronic health records.

Artificial intelligence in medicine
Electronic health record systems are ubiquitous and the majority of patients' data are now being collected electronically in the form of free text. Deep learning has significantly advanced the field of natural language processing and the self-supervi...

Data-level Linkage of Multiple Surveys for Improved Understanding of Global Health Challenges.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Data-driven approaches can provide more enhanced insights for domain experts in addressing critical global health challenges, such as newborn and child health, using surveys (e.g., Demographic Health Survey). Though there are multiple surveys on the ...

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing.

Annual review of biomedical data science
The COVID-19 (coronavirus disease 2019) pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread. Many of these difficulties are fund...

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.

Frontiers in public health
Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the me...

Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit.

Artificial intelligence in medicine
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that...

Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records.

Medical & biological engineering & computing
Coronary artery disease (CAD) is the major cause of human death worldwide. The development of new CAD early diagnosis methods based on medical big data has a great potential to reduce the risk of CAD death. In this process, neural network (NN), as a ...