AIMC Topic: Machine Learning

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Quality enhancement of batter-coated oysters via low temperature vacuum frying technique and machine learning-based prediction models for shelf life and quality dynamics.

Food chemistry
As a widely consumed prepared food, the preservation of nutrient retention and sensory quality in batter fried oysters is a significant concern. Deep frying, air frying, and vacuum frying (VF) were used to prepare battered fried oysters. Guar gum (GG...

Machine learning-based prediction model for post-ERCP cholangitis in patients with malignant biliary obstruction: a retrospective multicenter study.

Surgical endoscopy
BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is the preferred palliative treatment for patients with unresectable malignant biliary obstruction (MBO), which can relieve biliary obstruction and prolong survival. Post-ERCP cholangi...

Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.

Surgical endoscopy
BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanced gastric cancer (LAGC). However, precise models for accurate prognostic predictions are lacking. We aimed to utilize Cox regression and integrate va...

Development of an explainable machine learning model for predicting device-related pressure injuries in clinical settings.

BMC medical informatics and decision making
BACKGROUND: Device-related pressure injury (DRPI) is a prevalent and severe problem for patients using medical devices. Timely identification of patients at high risk of DRPI is crucial for healthcare providers to make informed decisions and prevent ...

Construction and evaluation of a height prediction model for children with growth disorders treated with recombinant human growth hormone.

BMC endocrine disorders
BACKGROUND: Height gain in children with growth disorders undergoing recombinant human growth hormone (rhGH) therapy shows considerable variability. Predicting treatment outcomes is essential for optimizing individualized treatment strategies.

A machine learning model reveals invisible microscopic variation in acute ischaemic stroke (≤ 6 h) with non-contrast computed tomography.

BMC medical imaging
BACKGROUND: In most medical centers, particularly in primary hospitals, non-contrast computed tomography (NCCT) serves as the primary imaging modality for diagnosing acute ischemic stroke. However, due to the small density difference between the infa...

An interpretable dynamic ensemble selection multiclass imbalance approach with ensemble imbalance learning for predicting road crash injury severity.

Scientific reports
Accurate prediction of crash injury severity and understanding the seriousness of multi-classification injuries is vital for informing authorities and the public. This Knowledge is crucial for enhancing road safety and reducing congestion, as differe...

Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques.

Scientific reports
Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate care. As the number of dementia cases continues to rise, delivering optimal care becomes more complex. ...

Anoikis-related biomarkers PARP1 and SDCBP as diagnostic and therapeutic targets for asthma.

Scientific reports
This study aims to explore the association between anoikis-related genes (ARGs) and asthma. The dataset GSE143303 for asthma were sourced from the GEO database, while ARGs were retrieved from the Harmonizome web portal and the GeneCards database. Dif...

Machine learning models for predicting multimorbidity trajectories in middle-aged and elderly adults.

Scientific reports
Multimorbidity has emerged as a significant public health issue in the context of global population aging. Predicting and managing the progression of multimorbidity in the elderly population is crucial. This study aims to develop predictive models fo...