AIMC Topic: Female

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A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa.

BMC health services research
BACKGROUND: The demand for quality healthcare is rising worldwide, and nurses in South Africa are under pressure to provide care with limited resources. This demanding work environment leads to burnout and exhaustion among nurses. Understanding the s...

Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study.

Journal of medical Internet research
BACKGROUND: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage o...

EC and EC of Remifentanil for Inhibiting Bronchoscopy Responses in Elderly Patients During Fiberoptic Bronchoscopy Under Ciprofol Sedation: An Up-and-Down Sequential Allocation Trial.

Drug design, development and therapy
BACKGROUND: Opioids are used to suppress cough during fiberoptic bronchoscopy (FOB). However, evidence regarding the optimal dose of remifentanil during FOB under ciprofol sedation is limited. This study aimed to investigate the effective concentrati...

Identifying the NEAT1/miR-26b-5p/S100A2 axis as a regulator in Parkinson's disease based on the ferroptosis-related genes.

PloS one
OBJECTIVES: Parkinson's disease (PD) is a complex neurodegenerative disease with unclear pathogenesis. Some recent studies have shown that there is a close relationship between PD and ferroptosis. We aimed to identify the ferroptosis-related genes (F...

Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao's older adult population through machine learning: A comprehensive analysis approach.

PloS one
BACKGROUND: Sarcopenia and reduced muscle quality index have garnered special attention due to their prevalence among older individuals and the adverse effects they generate. Early detection of these geriatric pathologies holds significant potential,...

Comparison of the spatial and temporal distribution of cutaneous and mucosal leishmaniasis in the state of Rio de Janeiro between 2001 and 2011.

PloS one
OBJECTIVE: To compare the spatio-temporal distribution of cutaneous leishmaniasis (CL) cases with mucosal leishmaniasis (ML) cases in the state of Rio de Janeiro (RJ) between 2001 and 2011.

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.

PloS one
BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI)...

Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan.

PloS one
Kawasaki Disease (KD) is a rare febrile illness affecting infants and young children, potentially leading to coronary artery complications and, in severe cases, mortality if untreated. However, KD is frequently misdiagnosed as a common fever in clini...

Experimental economics for machine learning-a methodological contribution on lie detection.

PloS one
In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new techn...

Computing nasalance with MFCCs and Convolutional Neural Networks.

PloS one
Nasalance is a valuable clinical biomarker for hypernasality. It is computed as the ratio of acoustic energy emitted through the nose to the total energy emitted through the mouth and nose (eNasalance). A new approach is proposed to compute nasalance...