AIMC Topic: Natural Language Processing

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Applications of artificial intelligence for patients with peripheral artery disease.

Journal of vascular surgery
OBJECTIVE: Applications of artificial intelligence (AI) have been reported in several cardiovascular diseases but its interest in patients with peripheral artery disease (PAD) has been so far less reported. The aim of this review was to summarize cur...

A multi-layer soft lattice based model for Chinese clinical named entity recognition.

BMC medical informatics and decision making
OBJECTIVE: Named entity recognition (NER) is a key and fundamental part of many medical and clinical tasks, including the establishment of a medical knowledge graph, decision-making support, and question answering systems. When extracting entities fr...

Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records.

Journal of the American Heart Association
Background Models predicting atrial fibrillation (AF) risk, such as Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF), have not performed as well in electronic health records. Natural language processing (NLP) may improve mo...

Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks.

Journal of biomedical informatics
In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequen...

Using deep learning to predict outcomes of legal appeals better than human experts: A study with data from Brazilian federal courts.

PloS one
Legal scholars have been trying to predict the outcomes of trials for a long time. In recent years, researchers have been harnessing advancements in machine learning to predict the behavior of natural and social processes. At the same time, the Brazi...

Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.

Basic & clinical pharmacology & toxicology
We sought to craft a drug safety signalling pipeline associating latent information in clinical free text with exposures to single drugs and drug pairs. Data arose from 12 secondary and tertiary public hospitals in two Danish regions, comprising appr...

Multidimensional Latent Semantic Networks for Text Humor Recognition.

Sensors (Basel, Switzerland)
Humor is a special human expression style, an important "lubricant" for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular resear...

Patient safety classifications, taxonomies and ontologies: A systematic review on development and evaluation methodologies.

Journal of biomedical informatics
INTRODUCTION: Patient safety classifications/ontologies enable patient safety information systems to receive and analyze patient safety data to improve patient safety. Patient safety classifications/ontologies have been developed and evaluated using ...

"Note Bloat" impacts deep learning-based NLP models for clinical prediction tasks.

Journal of biomedical informatics
One unintended consequence of the Electronic Health Records (EHR) implementation is the overuse of content-importing technology, such as copy-and-paste, that creates "bloated" notes containing large amounts of textual redundancy. Despite the rising i...