AIMC Topic: Female

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External validation of a machine learning prediction model for massive blood loss during surgery for spinal metastases: a multi-institutional study using 880 patients.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: A machine learning (ML) model was recently developed to predict massive intraoperative blood loss (>2,500 mL) during posterior decompressive surgery for spinal metastasis that performed well on external validation within the same ...

Control of dental calculus Prevents severe Radiation-Induced oral mucositis in patients undergoing radiotherapy for nasopharyngeal carcinoma.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: This study aims to develop an artificial intelligence model to predict severe radiation-induced oral mucositis (RIOM) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) and verify the risk factors associated with severe RIOM...

A large-scale multicenter study of reference intervals and clinical potential for homocysteine-folate cycle metabolites in Northern Chinese population.

Clinica chimica acta; international journal of clinical chemistry
OBJECTIVES: The study was conducted to establish the reference intervals of homocysteine-folate cycle metabolites based on the healthy population from multiple centers in northern China, and determine their clinical significance in the diagnosis of r...

Machine learning prediction models for multidrug-resistant organism infections in ICU ventilator-associated pneumonia patients: Analysis using the MIMIC-IV database.

Computers in biology and medicine
OBJECTIVE: This study aims to construct and compare four machine learning models using the MIMIC-IV database to identify high-risk factors for multidrug-resistant organism (MDRO) infection in Ventilator-associated pneumonia (VAP) patients.

[How older people are learning through artificial intelligence-assisted health technologies : Cute seals and nervous fall sensors].

Zeitschrift fur Gerontologie und Geriatrie
BACKGROUND: With the growing use of artificial intelligence (AI) in various areas of life, AI technologies are increasingly being developed for the nursing and care of older people and are intended to contribute to greater safety for older people in ...

Artificial Intelligence-Enhanced Analysis of Echocardiography-Based Radiomic Features for Myocardial Hypertrophy Detection and Etiology Differentiation.

Circulation. Cardiovascular imaging
BACKGROUND: While echocardiography is pivotal for detecting left ventricular hypertrophy (LVH), it struggles with etiology differentiation. To enhance LVH assessment, we aimed to develop an artificial intelligence algorithm using echocardiography-bas...

Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.

Applied immunohistochemistry & molecular morphology : AIMM
Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly...

Machine Learning Models predicting Decompensation in Cirrhosis.

Journal of gastrointestinal and liver diseases : JGLD
BACKGROUND AND AIMS: Decompensation of cirrhosis significantly decreases survival, thus, prevention of complications is paramount. We used machine learning techniques to identify parameters predicting decompensation.

Exploring the factors influencing the adoption of artificial intelligence technology by university teachers: the mediating role of confidence and AI readiness.

BMC psychology
OBJECTIVES: This study aims to explore the mediating role of confidence and artificial intelligence (AI) readiness in university teachers' behavioral intention to adopt AI technology, providing empirical support for enhancing teachers' willingness to...

A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.

BMC health services research
BACKGROUND: The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefo...