Cultural Competence

Machine Learning Algorithms to Estimate Propensity Scores in Health Policy Evaluation: A Scoping Review.

(1) Background: Quasi-experimental design has been widely used in causal inference for health policy impact evaluation. However, due to the non-randomized treatment used, there is great potential for bias in the assessment of the results, which can be reduced by using propensity score (PS) methods. In this context, this article aims to map the literature concerning the use of machine learning (ML)...

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels of generalization capability and interpretability. Intuitively, the inductive bias of an ML-based model for PLA prediction should fit in with biologica...

AmbiBias Contrast: Enhancing debiasing networks via disentangled space from ambiguity-bias clusters.

The goal of debiasing in classification tasks is to train models to be less sensitive to correlations between a sample's target attribution and period...

Knowledge-based inductive bias and domain adaptation for cell type annotation.

Measurement techniques often result in domain gaps among batches of cellular data from a specific modality. The effectiveness of cross-batch annotatio...

The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities - a scoping review.

This scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and th...

Automated speech recognition bias in personnel selection: The case of automatically scored job interviews.

Organizations, researchers, and software increasingly use automatic speech recognition (ASR) to transcribe speech to text. However, ASR can be less ac...

Effect of observer's cultural background and masking condition of target face on facial expression recognition for machine-learning dataset.

Facial expression recognition (FER) is significantly influenced by the cultural background (CB) of observers and the masking conditions of the target ...

Laboratory Data as a Potential Source of Bias in Healthcare Artificial Intelligence and Machine Learning Models.

Artificial intelligence (AI) and machine learning (ML) are anticipated to transform the practice of medicine. As one of the largest sources of digital...

Transfer Learning With Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-Centre Data.

Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited...

Ethicara for Responsible AI in Healthcare: A System for Bias Detection and AI Risk Management.

The increasing torrents of health AI innovations hold promise for facilitating the delivery of patient-centered care. Yet the enablement and adoption ...

Effectiveness of robotic rehabilitation for gait and balance in people with multiple sclerosis: a systematic review.

This review investigated the effectiveness of robotic-assisted gait training (RAGT) in improving gait and balance performance in adults with multiple ...

Disentangled contrastive learning for fair graph representations.

Graph Neural Networks (GNNs) play a key role in efficiently learning node representations of graph-structured data through message passing, but their ...

Applying a community-engaged participatory machine learning model.

Although predictive algorithms have been described as the definitive solution to bias in health care, machine learning techniques may also propagate e...

A deep learning-based approach for unbiased kinematic analysis in CNS injury.

Traumatic spinal cord injury (SCI) is a devastating condition that impacts over 300,000 individuals in the US alone. Depending on the severity of the ...

Improving viral annotation with artificial intelligence.

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their depend...

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

This study utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and End Results (SEER) database. Here, 10 different m...

Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions.

The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to ad...

Perspectives on AI use in medicine: views of the Italian Society of Artificial Intelligence in Medicine.

The first annual meeting of the Italian Society for Artificial Intelligence in Medicine (Società Italiana Intelligenza Artificiale in Medicina, SIIAM)...

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival.

Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of t...

Mitigating the risk of artificial intelligence bias in cardiovascular care.

Digital health technologies can generate data that can be used to train artificial intelligence (AI) algorithms, which have been particularly transfor...