AIMC Topic: Female

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Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice.

Scientific reports
Untreated neonatal jaundice can have severe consequences. Effective screening for neonatal jaundice can prevent long-term complications in infants. Non-invasive approaches may be beneficial in settings with limited resources. This feasibility study e...

Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time.

Scientific reports
In mental health, accurate symptom assessment and precise measurement of patient conditions are crucial for clinical decision-making and effective treatment planning. Traditional assessment methods can be burdensome, especially for vulnerable populat...

Improving Malaria diagnosis through interpretable customized CNNs architectures.

Scientific reports
Malaria, which is spread via female Anopheles mosquitoes and is brought on by the Plasmodium parasite, persists as a serious illness, especially in areas with a high mosquito density. Traditional detection techniques, like examining blood samples wit...

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.

Scientific reports
Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severit...

Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.

BMC medical informatics and decision making
BACKGROUND: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool...

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

Aging clinical and experimental research
BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine lea...

Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery.

Langenbeck's archives of surgery
PURPOSE: Several surgical-skill assessment tools emphasize the importance of efficient tissue-dissection, whose assessment relies on human judgment and is thus subject to bias. Automated assessment may help solve this problem. This study aimed to ver...

Biophysical versus machine learning models for predicting rectal and skin temperatures in older adults.

Journal of thermal biology
This study compares the efficacy of machine learning models to traditional biophysical models in predicting rectal (T) and skin (T) temperatures of older adults (≥60 years) during prolonged heat exposure. Five machine learning models were trained on ...

Advanced echocardiography and cluster analysis to identify secondary tricuspid regurgitation phenogroups at different risk.

Revista espanola de cardiologia (English ed.)
INTRODUCTION AND OBJECTIVES: Significant secondary tricuspid regurgitation (STR) is associated with poor prognosis, but its heterogeneity makes predicting patient outcomes challenging. Our objective was to identify STR prognostic phenogroups.

Population norms for the EQ-5D-5L for Hungary: comparison of online surveys and computer assisted personal interviews.

The European journal of health economics : HEPAC : health economics in prevention and care
BACKGROUND AND OBJECTIVES: The aims of this study were to provide population norms for EQ-5D-5L in Hungary and investigate the differences in EQ-5D-5L normative data by survey mode, i.e. online surveys and computer assisted personal interviews (CAPI)...