AIMC Topic: Adult

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Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors.

JCI insight
Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analyses of disease ...

Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

Clinical pharmacokinetics
INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C), as an indicator of safety and efficacy, are important for optimizing therapy.

Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity betwee...

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study.

International journal of medical informatics
BACKGROUND: Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial.

Navigating challenges and opportunities: Nursing student's views on generative AI in higher education.

Nurse education in practice
AIM: This qualitative study aims to explore the perspectives of nursing students regarding the application and integration of generative Artificial Intelligence (AI) tools in their studies.

A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.

Critical care (London, England)
BACKGROUND: Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technol...

The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation.

Reproductive biology and endocrinology : RB&E
OBJECTIVE: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms.

Prediction of COVID-19 patients' participation in financing informal care using machine learning methods: willingness to pay and willingness to accept approaches.

BMC health services research
BACKGROUND: Informal care plays an essential role in managing the COVID-19 pandemic. Expanding health insurance packages that reimburse caregivers' services through cost-sharing policies could increase financial resources. Predicting payers' willingn...

Cross-cultural comparison of beauty judgments in visual art using machine learning analysis of art attribute predictors among Japanese and German speakers.

Scientific reports
In empirical art research, understanding how viewers judge visual artworks as beautiful is often explored through the study of attributes-specific inherent characteristics or artwork features such as color, complexity, and emotional expressiveness. T...