AIMC Topic: Natural Language Processing

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Segment anything model for medical image segmentation: Current applications and future directions.

Computers in biology and medicine
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The recent introduction of the Segment Anything Model (SAM) signifies a noteworthy...

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.

BMC bioinformatics
BACKGROUND: Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the info...

Trends in stroke-related journals: Examination of publication patterns using topic modeling.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: This study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literatu...

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing.

Psychiatry research
Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using s...

Large language models for science and medicine.

European journal of clinical investigation
Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task-specific LLMs have demonstrated potential across diver...

Online biomedical named entities recognition by data and knowledge-driven model.

Artificial intelligence in medicine
Named entity recognition (NER) is an important task for the natural language processing of biomedical text. Currently, most NER studies standardized biomedical text, but NER for unstandardized biomedical text draws less attention from researchers. Na...

Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.

Neuroradiology
PURPOSE: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetit...

The marital and fertility sentiment orientation of Chinese women and its influencing factors - An analysis based on natural language processing.

PloS one
BACKGROUND: With the evolution of China's social structure and values, there has been a shift in attitudes towards marriage and fertility, with an increasing number of women holding diverse perspectives on these matters. In order to better comprehend...

Assessing Verbal Eyewitness Confidence Statements Using Natural Language Processing.

Psychological science
After an eyewitness completes a lineup, officers are advised to ask witnesses how confident they are in their identification. Although researchers in the lab typically study eyewitness confidence numerically, confidence in the field is primarily gath...