AIMC Topic: Machine Learning

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Machine learning model for prediction of palliative care phases in patients with advanced cancer: a retrospective study.

BMC palliative care
BACKGROUND: Developing an accurate predictive model for palliative care phases is crucial for improving cancer patient management, enabling healthcare providers to identify those in need of specific care plans and streamlining decision-making process...

Classifying athletes and non-athletes by differences in spontaneous brain activity: a machine learning and fMRI study.

Brain structure & function
Different types of sports training can induce distinct changes in brain activity and function; however, it remains unclear if there are commonalities across various sports disciplines. Moreover, the relationship between these brain activity alteratio...

Noninvasive prediction of failure of the conservative treatment in lateral epicondylitis by clinicoradiological features and elbow MRI radiomics based on interpretable machine learning: a multicenter cohort study.

Journal of orthopaedic surgery and research
OBJECTIVES: To develop and validate an interpretable machine learning model based on clinicoradiological features and radiomic features based on magnetic resonance imaging (MRI) to predict the failure of conservative treatment in lateral epicondyliti...

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.

BMC pediatrics
OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients.

Machine learning for predicting retention times of chiral analytes chromatographically separated by CMPA technique.

Journal of chromatography. A
Chiral mobile phase additive (CMPA) technique is an attractive method for chromatographic enantioseparation of chiral analytes. However, establishing chromatographic separation and analysis methods for given chiral analytes often requires extensive t...

Identification of molecular subtypes and a prognostic signature based on machine learning and purine metabolism-related genes in breast cancer.

Medicine
Breast cancer (BC), one of the most prevalent malignant tumors worldwide, lacks efficacious diagnostic biomarkers and therapeutic targets. This study harnesses multi-omics data to identify novel purine metabolism-related genes (PMRG) as potential bio...

Clinical prediction of pathological complete response in breast cancer: a machine learning study.

BMC cancer
BACKGROUND: This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients.

Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal study.

BMC public health
BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only physical health but also significantly increasing the risk of depression. This study aims to construc...

Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia.

Scientific reports
Abortion is a critical health issue that leads to numerous complications, maternal deaths, and significant financial burdens on women, families, and healthcare systems. Studies have identified factors associated with abortion using traditional statis...