AIMC Topic: Humans

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Integrating health equity in artificial intelligence for public health in Canada: a rapid narrative review.

Frontiers in public health
INTRODUCTION: The application of artificial intelligence (AI) in public health is rapidly evolving, offering promising advancements in various public health settings across Canada. AI has the potential to enhance the effectiveness, precision, decisio...

Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning.

Frontiers in endocrinology
BACKGROUND: Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health problem worldwide. There is evidence that endoplasmic reticulum stress (ERS) is involved in the pathog...

Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.

Frontiers in endocrinology
OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).

Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.

Frontiers in immunology
BACKGROUND: Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. Neoadjuvant therapy (NAT), administered prior to surgery, is integral to breast cancer treatment strategies....

Identification of prognostic subtypes and the role of FXYD6 in ovarian cancer through multi-omics clustering.

Frontiers in immunology
BACKGROUND: Ovarian cancer (OC), as a malignant tumor that seriously endangers the lives and health of women, is renowned for its complex tumor heterogeneity. Multi-omics analysis, as an effective method for distinguishing tumor heterogeneity, can mo...

Non-customized data asset evaluation based on knowledge graph and value entropy.

PloS one
With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-...

An evidence-based guidance framework for neural network system diagrams.

PloS one
Accurate communication of research is essential. We present the first evidence-based framework for formatting neural network architecture diagrams within scholarly publications. Neural networks are a prevalent and important machine learning component...

A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing.

PloS one
Recent advancements in hardware technology have spurred a surge in the popularity and ubiquity of wearable sensors, opening up new applications within the medical domain. This proliferation has resulted in a notable increase in the availability of Ti...

Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach.

PloS one
Only about half of patients with social anxiety disorder (SAD) respond substantially to cognitive behavioral therapy (CBT). However, there has been little evidence available to clinicians or patients about whether any individual patient is more or le...