Cultural Competence

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems ...

A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.

Automatic segmentation of the coronary artery using coronary computed tomography angiography (CCTA) ...

Supporting equitable and responsible highway safety improvement funding allocation strategies - Why AI prediction biases matter.

The existing methodologies for allocating highway safety improvement funding closely rely on the uti...

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk.

BACKGROUND: Buffalo milk, constituting 15% of global production, has higher fatty acids content than...

Contrastive Prototype-Guided Generation for Generalized Zero-Shot Learning.

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen classes, while only sam...

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T.

PURPOSE: Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due t...

Unsupervised Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering.

Pre-trained Language Model (PLM) is nowadays the mainstay of Unsupervised Sentence Representation Le...

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of ...

Inherent Bias in Large Language Models: A Random Sampling Analysis.

There are mounting concerns regarding inherent bias, safety, and tendency toward misinformation of l...

Understanding Bias in Artificial Intelligence: A Practice Perspective.

In the fall of 2021, several experts in this space delivered a Webinar hosted by the American Societ...

Prediction of polyspecificity from antibody sequence data by machine learning.

Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimi...

TNCB: Tri-Net With Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification.

In clinical settings, the implementation of deep neural networks is impeded by the prevalent problem...

Does Pollyanna hypothesis hold true in death narratives? A sentiment analysis approach.

Pollyanna hypothesis claims that human beings have a universal tendency to use positive words more f...

Minimizing bias when using artificial intelligence in critical care medicine.

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovaria...

Frequency compensated diffusion model for real-scene dehazing.

Due to distribution shift, deep learning based methods for image dehazing suffer from performance de...

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.

Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the ...

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis.

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivo...

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by ...

Can neural networks benefit from objectives that encourage iterative convergent computations? A case study of ResNets and object classification.

Recent work has suggested that feedforward residual neural networks (ResNets) approximate iterative ...

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationshi...