AIMC Topic: Machine Learning

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Optimized machine learning model for predicting unplanned reoperation after rectal cancer anterior resection.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Unplanned reoperation (URO) after surgery adversely affects the quality of life and prognosis of patients undergoing anterior resection for rectal cancer. This study aims to meet the urgent need for reliable predictive tools by developing...

The next revolution in computational simulations: Harnessing AI and quantum computing in molecular dynamics.

Current opinion in structural biology
The integration of artificial intelligence, machine learning and quantum computing into molecular dynamics simulations is catalyzing a revolution in computational biology, improving the accuracy and efficiency of simulations. This review describes th...

Differential plasma cytokine variation following X-ray or proton brain irradiation using machine-learning approaches.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: X-ray and proton irradiation have been reported to induce distinct modifications in cytokine expression in vitro and in vivo, suggesting a dissimilar inflammatory response between X-rays and protons. We aimed to investigate the differences i...

Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND & OBJECTIVE: The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medical system to predict and treat dengue patients. Hence, the present study used a metabolomic approach ...

Prediction of People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data.

Journal of diabetes science and technology
BACKGROUND AND AIMS: Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require ad...

Comparative evaluation of machine learning models in predicting overall survival for nasopharyngeal carcinoma using F-FDG PET-CT parameters.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: The objective of this study is to assess the prognostic efficacy of F-fluorodeoxyglucose (F-FDG) positron emission tomography/computed tomography (PET-CT) parameters in nasopharyngeal carcinoma (NPC) and identify the best machine learning (M...

Machine learning-enabled attapulgite/polyimide nanofiber composite aerogels-based colorimetric sensor array for real-time monitoring of balsa fish freshness.

Food chemistry
This paper presents the development and application of attapulgite/polyimide nanofiber composite aerogels (ATP/PI NFAs) integrated with a range of acid-base indicators, fabricated using electrospinning and freeze-drying technologies. A detailed chara...

Spatiotemporal modelling of airborne birch and grass pollen concentration across Switzerland: A comparison of statistical, machine learning and ensemble methods.

Environmental research
BACKGROUND: Statistical and machine learning models are commonly used to estimate spatial and temporal variability in exposure to environmental stressors, supporting epidemiological studies. We aimed to compare the performances, strengths and limitat...