AIMC Topic: Machine Learning

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Innovative application of confocal Raman spectroscopy and Machine learning in cardiovascular diseases identification.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Myocardial hypertrophy and heart failure are leading causes of mortality in cardiovascular diseases, yet current diagnostic techniques lack the resolution to monitor molecular changes effectively. In this study, we employed confocal Raman spectroscop...

Predicting Urine Culture Outcomes in Adult Patients Using Machine Learning with the Aim of Reducing Unnecessary Urine Cultures.

The journal of applied laboratory medicine
BACKGROUND: Urine cultures are frequently ordered tests with a low positivity rate. Development of a machine learning model to predict urine culture outcomes could not only reduce unnecessary urine cultures but also prevent preliminary antibiotic tre...

Leveraging Hematologic Single-Cell Measurements for Patient Triage and Outcome Prediction.

The journal of applied laboratory medicine
BACKGROUND: The complete blood count (CBC) is widely used across nearly all areas of medicine. While standard CBC markers reflect basic summaries of the blood cells, modern hematology analyzers generate many additional markers from the underlying dat...

FDG-PET Intensity Normalization Improves Radiomics-Based Survival Prediction in Patients with Oropharyngeal Cancer: A Comparison of the Standardized Uptake Value with Alternative Normalization Techniques.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Despite the widespread research application of radiomics, there is a knowledge gap regarding the optimal voxel intensity normalization strategy for FDG-PET radiomics. We investigated the impact of 3 normalization strategies on...

Unravelling the multifaceted actions of neurosteroids: Machine learning and in vitro screening for novel target discovery.

British journal of pharmacology
BACKGROUND AND PURPOSE: Neurosteroids (NS) modulate neuronal function and are promising therapeutic agents for neuropsychiatric disorders. NS analogues are approved for treating postpartum depression and are of interest in other disorders. Gamma-amin...

Identifying New Candidate Predictors of Mortality in Japanese Patients with Severe Drug Eruptions.

Drug safety
UNLABELLED: BACKGROUND AND OBJECTIVES: SCORe of Toxic Epidermal Necrolysis (SCORTEN) and ABCD-10 have been developed as scoring systems for predicting mortality associated with Stevens-Johnson syndrome (SJS) or toxic epidermal necrolysis (TEN). These...

Machine learning using serial changes in proteinuria during initial steroid therapy to predict treatment response and immunosuppressant use in pediatric idiopathic nephrotic syndrome.

Clinical and experimental nephrology
BACKGROUND: Epidemiological studies on idiopathic nephrotic syndrome (INS) in children have identified no definitive factors predicting steroid-resistant nephrotic syndrome (SRNS) or frequent relapsing nephrotic syndrome. Research using machine learn...

Patterns in Mental Health Symptoms, Substance Use, and Viral Suppression in People with HIV: A Clustering Analysis.

AIDS and behavior
Mental health conditions and substance use are prevalent among people with HIV (PWH), are correlated with one another, and associate with viral non-suppression independently; their joint association with viral non-suppression may be under-studied bec...

A mechanism study on laser-induced breakdown spectroscopy and machine learning-based characterization method for waste organic polymers.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA
The method based on machine learning and laser-induced breakdown spectroscopy (LIBS) is effective for rapid characterization of waste organic polymers (WOP). However, the lack of mechanistic interpretability leads to raises concerns regarding its rel...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression in patients with major depressive disorder (MDD) and bipolar disorder (BD), but accurate prediction of treatment response remains a challenge. Th...