AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Semantics

Showing 91 to 100 of 1349 articles

Clear Filters

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation.

Journal of biomedical informatics
BACKGROUND: Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records ...

PhraseAug: An Augmented Medical Report Generation Model With Phrasebook.

IEEE transactions on medical imaging
Medical report generation is a valuable and challenging task, which automatically generates accurate and fluent diagnostic reports for medical images, reducing workload of radiologists and improving efficiency of disease diagnosis. Fine-grained align...

Instant prediction of scientific paper cited potential based on semantic and metadata features: Taking artificial intelligence field as an example.

PloS one
With the continuous increase in the number of academic researchers, the volume of scientific papers is also increasing rapidly. The challenge of identifying papers with greater potential academic impact from this large pool has received increasing at...

Assessing AI Simplification of Medical Texts: Readability and Content Fidelity.

International journal of medical informatics
INTRODUCTION: The escalating complexity of medical literature necessitates tools to enhance readability for patients. This study aimed to evaluate the efficacy of ChatGPT-4 in simplifying neurology and neurosurgical abstracts and patient education ma...

Strongly concealed adversarial attack against text classification models with limited queries.

Neural networks : the official journal of the International Neural Network Society
In black-box scenarios, adversarial attacks against text classification models face challenges in ensuring highly available adversarial samples, especially a high number of invalid queries under long texts. The existing methods select distractors by ...

Generalized zero-shot learning via discriminative and transferable disentangled representations.

Neural networks : the official journal of the International Neural Network Society
In generalized zero-shot learning (GZSL), it is required to identify seen and unseen samples under the condition that only seen classes can be obtained during training. Recent methods utilize disentanglement to make the information contained in visua...

Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors.

Scientific reports
To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs) patients. We retrospectively collected...

Quantum-inspired neural network with hierarchical entanglement embedding for matching.

Neural networks : the official journal of the International Neural Network Society
Quantum-inspired neural networks (QNNs) have shown potential in capturing various non-classical phenomena in language understanding, e.g., the emgerent meaning of concept combinations, and represent a leap beyond conventional models in cognitive scie...

Multilevel semantic and adaptive actionness learning for weakly supervised temporal action localization.

Neural networks : the official journal of the International Neural Network Society
Weakly supervised temporal action localization aims to identify and localize action instances in untrimmed videos with only video-level labels. Typically, most methods are based on a multiple instance learning framework that uses a top-K strategy to ...

Reconsidering learnable fine-grained text prompts for few-shot anomaly detection in visual-language models.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Anomaly Detection (FSAD) in industrial images aims to identify abnormalities using only a few normal images, which is crucial for industrial scenarios where sample training is limited. The recent advances in large-scale pre-trained visual-la...