AIMC Topic: Female

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Leveraging unsupervised machine learning techniques for detecting outliers in the daily milk yield data of dairy cows.

Journal of dairy science
The lactation curve is essential for developing effective feeding plans, optimizing breeding, and strategizing milk production for dairy farms. However, health disorders, as well as external factors such as heat stress, dietary changes, and certain m...

Developing the CAM-BERT: Enhancing delirium screening in hospitalized older adults using natural language processing.

Computers in biology and medicine
BACKGROUND: Delirium is a common condition affecting hospitalized older adults, often leading to adverse outcomes. Nevertheless, delirium frequently goes unrecognized due to various clinical and systemic challenges. We aimed to develop and evaluate a...

Comparison of synthetic LGE with optimal inversion time vs. conventional LGE via representation learning: Quantification of Bias in Population Analysis.

Computers in biology and medicine
PURPOSE: Late Gadolinium Enhancement (LGE) images are crucial elements of CMR protocols for evaluating myocardial infarct (MI) severity and size. However, these images rely on signal intensity changes and manual inversion time (TI) settings, leading ...

Beyond traditional models: Jaya-optimized ensembles for accurate heart disease prediction.

Computers in biology and medicine
INTRODUCTION: Heart Disease (HD) stands as the foremost reason for mortality all over the world for both men and women. Millions of people are affected worldwide every year, resulting in numerous fatalities. Timely and precise detection is essential ...

Association of artificial intelligence-predicted milk yield residuals to behavioral patterns and transition success in multiparous dairy cows.

Journal of dairy science
Data-driven health monitoring based on milk yield has shown potential to identify health-perturbing events during the transition period. As a proof of principle, we explored the association between the cow's residual milk yield, that is, the differen...

Machine Learning Prediction of Financial Toxicity in Patients with Resected Lung Cancer.

Journal of the American College of Surgeons
BACKGROUND: Financial toxicity (FT) refers to the financial stress and detrimental impact on quality of life experienced by patients due to treatment cost. In patients with resected lung cancer (LC), we sought to identify those at risk of developing ...

Machine Learning-Based Screening of Cosmetic Ingredients Identifies Vat Blue 6 as a Thyroid Hormone Receptor β Disruptor.

Environmental science & technology
Thyroid disorders are among the most prevalent endocrine conditions worldwide, exhibiting a rising incidence and disproportionately affecting women. In this study, we hypothesized that cosmetics may contain previously unidentified thyroid-disrupting ...

Which explanations do clinicians prefer? A comparative evaluation of XAI understandability and actionability in predicting the need for hospitalization.

BMC medical informatics and decision making
BACKGROUND: This study aims to address the gap in understanding clinicians' attitudes toward explainable AI (XAI) methods applied to machine learning models using tabular data, commonly found in clinical settings. It specifically explores clinicians'...

Knowledge and perception of artificial lntelligence education among undergraduate healthcare students.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is gaining recognition for its ability to enhance patient outcomes in healthcare. Therefore, integrating AI into the undergraduate curriculum is essential to equip students with foundational knowledge before g...

Screening for endometriosis: A scoping review of screening measures that could support early diagnosis.

BMC women's health
BACKGROUND: Endometriosis is prevalent in approximately 6-10% of all women of reproductive age and is associated with pelvic pain, heavy menstrual bleeding, infertility, and pain during intercourse. Despite reporting symptoms, women wait around 11 ye...