AIMC Topic: Machine Learning

Clear Filters Showing 13431 to 13440 of 34417 articles

Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.

Cancer medicine
BACKGROUND: In those receiving chemotherapy, renal and hepatic dysfunction can increase the risk of toxicity and should therefore be monitored. We aimed to develop a machine learning model to identify those patients that need closer monitoring, enabl...

AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.

Journal of medical systems
Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to ...

Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015-2020 in Shaanxi, China.

Journal of environmental sciences (China)
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on s...

Identifying multilevel predictors of trajectories of psychopathology and resilience among juvenile offenders: A machine learning approach.

Development and psychopathology
Mental ill health is more common among juvenile offenders relative to adolescents in general. Little is known about individual differences in their long-term psychological adaptation and its predictors from multiple aspects of their life. This study ...

Artificial intelligence for dementia genetics and omics.

Alzheimer's & dementia : the journal of the Alzheimer's Association
Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to addre...

Bioactivation and reactivity research advances - 2022 year in review‡.

Drug metabolism reviews
With the 50th year mark since the launch of journal, the field of drug metabolism and bioactivation has advanced exponentially in the past decades (Guengerich 2023).This has, in a major part, been due to the continued advances across the whole spect...

Individual-Level Prediction of Exposure Therapy Outcome Using Structural and Functional MRI Data in Spider Phobia: A Machine-Learning Study.

Depression and anxiety
Machine-learning prediction studies have shown potential to inform treatment stratification, but recent efforts to predict psychotherapy outcomes with clinical routine data have only resulted in moderate prediction accuracies. Neuroimaging data showe...

Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI.

Medical physics
BACKGROUND: Artificial intelligence/computer-aided diagnosis (AI/CADx) and its use of radiomics have shown potential in diagnosis and prognosis of breast cancer. Performance metrics such as the area under the receiver operating characteristic (ROC) c...

Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery.

Journal of chemical information and modeling
In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of new chemical entities is one of the most critical issues, which is a time-consuming process, immensely expensive, ...

Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin and Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial.

The American journal of medicine
BACKGROUND: Atrial fibrillation and heart failure commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with heart failure at risk of developing atrial fibrillation would allow clinicians the opportunity to i...