AIMC Topic: Female

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Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study.

JMIR medical informatics
BACKGROUND: Bipolar disorder (BD) is a highly recurrent disorder. Early detection, early intervention, and prevention of recurrent bipolar mood symptoms are key to a better prognosis.

Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study.

Journal of medical Internet research
BACKGROUND: Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based appro...

Real-Time Estimation of Arterial Partial Pressure of Carbon Dioxide in Patients Undergoing General Anesthesia: Predictive Modeling Study.

JMIR medical informatics
BACKGROUND: Adequate ventilation in mechanically ventilated patients is contingent upon the monitoring of the arterial partial pressure of carbon dioxide (PaCO2) during general anesthesia. Despite its significance, continuous monitoring remains chall...

Submovements Derived from Wearable Sensors Capture Ataxia Severity and Differ Across Motor Tasks and Directions of Motion.

Cerebellum (London, England)
Digital measures derived from wearable sensors are a promising approach for assessing motor impairment in clinical trials. Submovements, which are velocity curves extracted from time series data, have been successful in characterizing impaired moveme...

Clinical validation of a deep learning based application for quantitative assessment of dental plaque in fluorescence imaging.

Clinical oral investigations
AIM: Evaluating dental plaque is a fundamental task for periodontal health care, but it is subjective, time-consuming, and cumbersome. Therefore, this study aimed to develop and validate a web-based deep learning application capable of objectively qu...

Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning.

Journal of cancer research and clinical oncology
PURPOSE: Hepatocellular carcinoma (HCC) is a perilous malignant tumor, and transcatheter arterial chemoembolization (TACE) is a widely adopted treatment technique for advanced HCC. Nevertheless, TACE may not effectively reduce the risk of distant met...

A deep learning approach to gender equality: Forecasting educational indicators with 1D-CNN aligned with SDG 5.

PloS one
Sustainable development goal (SDG) 5 focuses on gender equality and empowerment and it is considered as one of the most important SDGs. Therefore, this article presented a time series prediction model that predicts gender-related educational results ...

Advantages of fully automated AI-enhanced algorithm (5D CNS+™) for generating a fetal neurosonogram in clinical routine.

Journal of perinatal medicine
OBJECTIVES: The objective was to demonstrate superiority of a fully vs. semi-automated approach (5D CNS+™) and to verify operators could handle and benefit from a fully automated rendering volumetric datasets to generate a complete fetal neurosonogra...

Multimodal MRI analysis selecting key brain features for machine learning based classification of diabetic neuropathic pain and phenotypes.

Journal of the neurological sciences
Cerebral alterations are associated with diabetic peripheral neuropathy (DPN) and neuropathic pain, including reductions in brain volumes, cortical thickness, sulcus depth, and alterations in metabolites and functional connectivity. This study combin...

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...