AIMC Topic: Machine Learning

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Ecological Network Analysis: Utilizing Machine Learning to Unravel the Effects of Multilevel Pathways of Moderate⁃to⁃Vigorous Physical Activity Facilitators Among School Children.

Research quarterly for exercise and sport
The objective of the present study was to ascertain whether the association between moderate-to-vigorous intensity physical activity (MVPA) levels and individual, interpersonal, organizational, and environmental factors among school children is influ...

Assessing subvisible particle risks in monoclonal antibodies: insights from quartz crystal microbalance with dissipation, machine learning, and in silico analysis.

mAbs
Throughout the lifecycle of biopharmaceutical development and manufacturing, monoclonal antibodies (mAbs) are subjected to diverse interfacial stresses and encounter various container surfaces. These interactions can cause the formation of subvisible...

Artificial intelligence predicts pregnancy complications based on cytokine profiles.

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
BACKGROUND: Early prediction of pregnancy complications is important for adequate and timely prevention, management, and reducing maternal/fetal pathogenesis.

Development of a novel machine learning model to automate vertebral column segmentation utilizing biplanar full-body imaging.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Degenerative scoliosis (DS) is a common spinal disorder among adults, characterized by lateral curvature of the spine. Recent advancements in biplanar full-body imaging, a low-dose and weight-bearing X-ray modality, facilitate saf...

Explainable artificial intelligence for prediction of refractory ulcerative colitis: analysis of a Japanese Nationwide Registry.

Annals of medicine
OBJECTIVE: Ulcerative colitis (UC) is a chronic inflammatory bowel disease for which remission is dependent on corticosteroid (CS) treatment. The diversity of disease pathophysiology necessitates optimal case-specific treatment selection. This study ...

A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study.

Emerging microbes & infections
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that imposes a considerable medical burden. In this study, we enrolled 1,606 SFTS patients, developed and validated machine learning models for mortality prediction,...

Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.

Renal failure
BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning mo...

Development and validation of multi-center serum creatinine-based models for noninvasive prediction of kidney fibrosis in chronic kidney disease.

Renal failure
OBJECTIVE: Kidney fibrosis is a key pathological feature in the progression of chronic kidney disease (CKD), traditionally diagnosed through invasive kidney biopsy. This study aimed to develop and validate a noninvasive, multi-center predictive model...

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.

Annals of medicine
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method fo...