AI Medical Compendium Topic

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

Datasets as Topic

Showing 281 to 290 of 1079 articles

Clear Filters

A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction.

Neural networks : the official journal of the International Neural Network Society
As a major method for relation extraction, distantly supervised relation extraction (DSRE) suffered from the noisy label problem and class imbalance problem (these two problems are also common for many other NLP tasks, e.g., text classification). How...

Ontology-driven weak supervision for clinical entity classification in electronic health records.

Nature communications
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical ...

Semi-supervised learning for an improved diagnosis of COVID-19 in CT images.

PloS one
Coronavirus disease 2019 (COVID-19) has been spread out all over the world. Although a real-time reverse-transcription polymerase chain reaction (RT-PCR) test has been used as a primary diagnostic tool for COVID-19, the utility of CT based diagnostic...

U-net model for brain extraction: Trained on humans for transfer to non-human primates.

NeuroImage
Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed...

Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.

Nature communications
Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervise...

Evaluation of supervised machine-learning methods for predicting appearance traits from DNA.

Forensic science international. Genetics
The prediction of human externally visible characteristics (EVCs) based solely on DNA information has become an established approach in forensic and anthropological genetics in recent years. While for a large set of EVCs, predictive models have alrea...

Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

American journal of human genetics
The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process oft...

Raising the Bar for Randomized Trials Involving Artificial Intelligence: The SPIRIT-Artificial Intelligence and CONSORT-Artificial Intelligence Guidelines.

The Journal of investigative dermatology
Artificial intelligence (AI)-based applications have the potential to improve the quality and efficiency of patient care in dermatology. Unique challenges in the development and validation of these technologies may limit their generalizability and re...

Deep learning approach to skin layers segmentation in inflammatory dermatoses.

Ultrasonics
Monitoring skin layers with medical imaging is critical to diagnosing and treating patients with chronic inflammatory skin diseases. The high-frequency ultrasound (HFUS) makes it possible to monitor skin condition in different dermatoses. Accurate an...

Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.

Proteins
Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better predict...