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Cultural Competence

Latest AI and machine learning research in cultural competence for healthcare professionals.

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Showing 904-924 of 3,669 articles
Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain...

The Moral Choice Machine.

Allowing machines to choose whether to kill humans would be devastating for world peace and security...

Correlation of Measured and Estimated Creatinine Clearance in Hospitalized Elderly Patients: A Retrospective Cohort Study.

Accurate assessment of renal function is essential in hospitalized elderly patients. Few studies ha...

Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys.

Modern survey methods may be subject to non-observable bias, from various sources. Among online surv...

Topics and trends in artificial intelligence assisted human brain research.

Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with...

Errors in femoral anteversion, femoral offset, and vertical offset following robot-assisted total hip arthroplasty.

The objectives were to determine errors in femoral anteversion (FA), femoral offset (FO), and vertic...

Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare.

The objective of this article is to discuss the inherent bias involved with artificial intelligence-...

Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections.

Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or ...

Comparing machine and human reviewers to evaluate the risk of bias in randomized controlled trials.

BACKGROUND: Evidence from new health technologies is growing, along with demands for evidence to inf...

Nonlinear dynamics based machine learning: Utilizing dynamics-based flexibility of nonlinear circuits to implement different functions.

The core element of machine learning is a flexible, universal function approximator that can be trai...

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable ...

On the localness modeling for the self-attention based end-to-end speech synthesis.

Attention based end-to-end speech synthesis achieves better performance in both prosody and quality ...

Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

Computational predictions of ligand binding is a difficult problem, with more accurate methods being...

Towards a global understanding of the drivers of marine and terrestrial biodiversity.

Understanding the distribution of life's variety has driven naturalists and scientists for centuries...

Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation.

The accuracy and robustness of image classification with supervised deep learning are dependent on t...

Learning Personalized Treatment Rules from Electronic Health Records Using Topic Modeling Feature Extraction.

To address substantial heterogeneity in patient response to treatment of chronic disorders and achie...

Projection Space Implementation of Deep Learning-Guided Low-Dose Brain PET Imaging Improves Performance over Implementation in Image Space.

Our purpose was to assess the performance of full-dose (FD) PET image synthesis in both image and si...

Detecting prolonged sitting bouts with the ActiGraph GT3X.

The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture...

Representation learning for clinical time series prediction tasks in electronic health records.

BACKGROUND: Electronic health records (EHRs) provide possibilities to improve patient care and facil...

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and fol...

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