AIMC Topic: Female

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A review of artificial intelligence applications in in vitro fertilization.

Journal of assisted reproduction and genetics
The field of reproductive medicine has witnessed rapid advancements in artificial intelligence (AI) methods, which have significantly enhanced the efficiency of diagnosing and treating reproductive disorders. The integration of AI algorithms into the...

Machine learning prediction of unexpected readmission or death after discharge from intensive care: A retrospective cohort study.

Journal of clinical anesthesia
BACKGROUND: Intensive care units (ICUs) harbor the sickest patients with the utmost needs of medical care. Discharge from ICU needs to consider the reason for admission and stability after ICU care. Organ dysfunction or instability after ICU discharg...

Identification of cultural conversations in therapy using natural language processing models.

Psychotherapy (Chicago, Ill.)
Researchers have historically focused on understanding therapist multicultural competency and orientation through client self-report measures and behavioral coding. While client perceptions of therapist cultural competency and multicultural orientati...

A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.

Journal of Alzheimer's disease : JAD
BACKGROUND: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors.

Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study.

Frontiers in public health
INTRODUCTION: Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number regaining full arm use. Robotic and technological rehabilitation has be...

Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study.

PeerJ
BACKGROUND: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment cho...

Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study.

Einstein (Sao Paulo, Brazil)
BACKGROUND: Beatriz Nistal-Nuño designed a machine learning system type of ensemble learning for patients undergoing cardiac surgery and intensive care unit cardiology patients, based on sequences of cardiovascular physiological measurements and othe...

An Ensemble Learning Algorithm for Cognitive Evaluation by an Immersive Virtual Reality Supermarket.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychological tests, commonly used for MCI detection, often lack ecological validity due to their simplistic a...

Determining domestic violence against women using machine learning methods: The case of Türkiye.

Journal of evaluation in clinical practice
BACKGROUND: Domestic violence against women is a pervasive issue globally, representing a severe violation of human rights and a significant public health concern. The hidden nature of such violence and its frequent underreporting make it a critical ...

A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region for brain PET scans with the aim of reducing interscan and intersubject variability. By training a binary linear SVM classifier with PET datasets from t...