AIMC Topic: Female

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Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.

Head & neck
BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (...

Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: A preliminary study.

Journal of dental education
PURPOSE/OBJECTIVES: Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clin...

Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and fu...

Artificial Intelligence to Predict the Risk of Lymph Node Metastasis in T2 Colorectal Cancer.

Annals of surgery
OBJECTIVE: To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC).

Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?

Neuroinformatics
Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to captu...

Predictive modeling of mortality in carbapenem-resistant bloodstream infections using machine learning.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompting a deeper exploration of treatment alternatives due to escalating carbapenem resistance. This study meticulously examined clinical, microbiological...

The impact of high-order features on performance of radiomics studies in CT non-small cell lung cancer.

Clinical imaging
High-order radiomic features have been shown to produce high performance models in a variety of scenarios. However, models trained without high-order features have shown similar performance, raising the question of whether high-order features are wor...

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection.

Clinical imaging
PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and cli...

Predicting recurrent gestational diabetes mellitus using artificial intelligence models: a retrospective cohort study.

Archives of gynecology and obstetrics
BACKGROUND: We aimed to develop novel artificial intelligence (AI) models based on early pregnancy features to forecast the likelihood of recurrent gestational diabetes mellitus (GDM) before 14 weeks of gestation in subsequent pregnancies.

Automated detection of tonic seizures using wearable movement sensor and artificial neural network.

Epilepsia
Although several validated wearable devices are available for detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neu...