AIMC Topic: Female

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Mortality risk prediction in NSTE-ACS following PCI: Insights from a real-world cohort.

PloS one
BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a major contributor to cardiovascular mortality, yet reliable tools for individualized mortality prediction remain limited. Machine learning offers the potential to enhance pr...

Implementation of a scientific approach to intrapartum care: the Emergency Caesarean Section-Decision Optimising Tool (EC-DOT) to eliminate avoidable harm to mothers and babies.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
Safe intrapartum care requires masterly observation, timely interventions, verbalization and escalation (MOTIVE) to optimize maternal and perinatal outcomes. In clinical situations where continuation of labor is deemed likely to worsen maternal and p...

Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.

Annals of medicine
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...

Correlation between atherogenic index of plasma and retinal vessels in the fundus: a cross-sectional study.

European journal of medical research
BACKGROUND: The Atherogenic Index of Plasma (AIP) is a novel logarithmic index that combines fasting triglyceride and high-density lipoprotein cholesterol (HDL-C) concentrations and is associated with the burden of atherosclerosis. Currently, the non...

A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis.

BMC medical informatics and decision making
Cervical cancer is the leading cause of cancer-related deaths among women worldwide, necessitating early and accurate detection methods. This study introduces a hybrid framework utilizing Vision Transformers (ViT) and ensemble learning-based convolut...

Cracking the code: a head-to-head comparison of expert clinicians and artificial intelligence in diagnosing rare diseases.

Orphanet journal of rare diseases
BACKGROUND: Patients with rare diseases often face prolonged diagnostic journeys due to the low prevalence and diverse clinical presentations of these conditions. In Germany, specialized centers for rare diseases, established at university hospitals,...

Predictive variables analysis for the tongue crib treatment of anterior crossbite in mixed dentition.

BMC oral health
OBJECTIVE: This study aimed to identify key prognostic variables and to develop and validate a clinical prediction model for pre-treatment assessment of tongue crib applicability.

Artificial intelligence-based chatbots improve the efficiency of course orientation among medical students: a cross-sectional study.

BMC medical education
BACKGROUND: Large language models (LLMs) like ChatGPT offer new ways to improve academic and administrative workflows in medical education, particularly for students studying in a language that is not their native tongue. We set out to examine whethe...

Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.

BMC medical research methodology
BACKGROUND: Mild cognitive impairment (MCI) represents a transitional stage to Alzheimer's disease (AD), making progression prediction crucial for timely intervention. Predictive models integrating clinical, laboratory, and survival data can enhance ...