AIMC Topic: Female

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The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol.

BMJ open
INTRODUCTION: Perinatal depression and anxiety (PDA) is associated with a high risk of maternal mortality. Existing data shows that 95% of maternal mortality in low- and middle-income countries (LMICs) is due to resource constraints and negligence in...

Relationship prediction between clinical subtypes and prognosis of critically ill patients with cirrhosis based on unsupervised learning methods: A study from two critical care databases.

International journal of medical informatics
BACKGROUND: Our objective was to identify distinct clinical subtypes among critically ill patients with cirrhosis and analyze the clinical features and prognosis of each subtype.

Utilizing machine learning for predicting mortality in patients with heat-related illness who visited the emergency department.

International journal of medical informatics
BACKGROUND: In the context of climate change and global warming, heat-related illness (HRI) is anticipated to escalate and become a major concern. Patients with severe HRI primarily present to the emergency department (ED), but there are no predictio...

Using longitudinal data and deep learning models to enhance resource allocation in home-based medical care.

International journal of medical informatics
BACKGROUND: The aging population is driving increased healthcare demands and costs, prompting the need for effective home healthcare programs. Accurate patient assessment is essential for optimizing resource allocation and tailoring services.

Artificial intelligence in asthma health literacy: a comparative analysis of ChatGPT versus Gemini.

The Journal of asthma : official journal of the Association for the Care of Asthma
BACKGROUND: Asthma is a complex and heterogeneous chronic disease affecting over 300 million individuals worldwide. Despite advances in pharmacotherapy, poor disease control remains a major challenge, necessitating innovative approaches to patient ed...

Automated detection and recognition of oocyte toxicity by fusion of latent and observable features.

Journal of hazardous materials
Oocyte quality is essential for successful pregnancy, yet no discriminant criterion exists to assess the effects of environmental pollutants on oocyte abnormalities. We developed a stepwise framework integrating deep learning-extracted latent feature...

Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.

Biochemical and biophysical research communications
Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and divisio...

Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of th...

Altered static and dynamic functional network connectivity and combined Machine learning in asthma.

Neuroscience
Asthma is a reversible disease characterized by airflow limitation and chronic airway inflammation. Previous neuroimaging studies have shown structural and functional abnormalities in the brains of individuals with asthma. However, earlier research h...