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Found 11 papers, 6 papers with code
Resilience of Named Entity Recognition Models under Adversarial Attack
Named entity recognition (NER) is a popular language processing task with wide applications.
Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media
Substance use disorders (SUDs) are a growing concern globally, necessitating enhanced understanding of the problem and its trends through data-driven research.
CARE-SD: Classifier-based analysis for recognizing and eliminating stigmatizing and doubt marker labels in electronic health records: model development and validation
Discussion: This study demonstrated the feasibility of supervised classifiers in automatically identifying stigmatizing labels and doubt markers in medical text, and identified trends in stigmatizing language use in an EHR setting.
Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias
Alzheimer's disease is a progressive, debilitating neurodegenerative disease that affects 50 million people globally.
Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies
Here we present the first method for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and pulse sequence.
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset.
Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham
We also applied a method for quantifying uncertainty across an ensemble of these models to automatically exclude out-of-distribution data in the AD detection.
Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining
We show that this method is considerably more robust than SynthSeg, while also outperforming cascaded networks and state-of-the-art segmentation denoising methods.
NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing
Dementia related cognitive impairment (CI) is a neurodegenerative disorder, affecting over 55 million people worldwide and growing rapidly at the rate of one new case every 3 seconds.
Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records
Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data.
Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning
Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data.
