AI Medical Compendium Topic

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

Information Storage and Retrieval

Showing 171 to 180 of 676 articles

Clear Filters

SpiNet - A FrameNet-like Schema for Automatic Information Extraction about Spine from Scientific Papers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
New medical research concerning the spine and its diseases are incrementally made available through biomedical literature repositories. Several Natural Language Processing (NLP) tasks, like Semantic Role Labelling (SRL) and Information Extraction (IE...

Natural language processing was effective in assisting rapid title and abstract screening when updating systematic reviews.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVE: To examine whether the use of natural language processing (NLP) technology is effective in assisting rapid title and abstract screening when updating a systematic review.

Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework.

Journal of medical Internet research
BACKGROUND: Liver cancer is a substantial disease burden in China. As one of the primary diagnostic tools for detecting liver cancer, dynamic contrast-enhanced computed tomography provides detailed evidences for diagnosis that are recorded in free-te...

Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network.

BioMed research international
Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in ...

Semi-supervised disentangled framework for transferable named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale la...

Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities.

JAMA network open
IMPORTANCE: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.

Bridging multimedia heterogeneity gap via Graph Representation Learning for cross-modal retrieval.

Neural networks : the official journal of the International Neural Network Society
Information retrieval among different modalities becomes a significant issue with many promising applications. However, inconsistent feature representation of various multimedia data causes the "heterogeneity gap" among various modalities, which is a...

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices.

Scientific reports
Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. How...

List-wise learning to rank biomedical question-answer pairs with deep ranking recursive autoencoders.

PloS one
Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typical...

LDA filter: A Latent Dirichlet Allocation preprocess method for Weka.

PloS one
This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text representation. LDA assumes that each document deals with a set o...