AIMC Topic: Machine Learning

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A scientometric analysis of machine learning in schizophrenia neuroimaging: Trends and insights (2012-2024).

Journal of affective disorders
Machine learning applications in schizophrenia neuroimaging research have undergone significant evolution since 2012. However, a comprehensive scientometric analysis of this field has not yet been conducted. This study analyzed 315 original research ...

Functional connectome-based predictive modeling of suicidal ideation.

Journal of affective disorders
Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanisms of suicidal behavior. We apply a connectome predictive modeling machine learning approach to ident...

Evaluation of Diagnostic Recommendations Embedded in Medication Alerts: Prospective Single-Arm Interventional Study.

Journal of medical Internet research
BACKGROUND: Potentially inappropriate prescribing in outpatient care contributes to adverse outcomes and health care inefficiencies. Clinical decision support systems (CDSS) offer promising solutions, but their effectiveness is often constrained by i...

Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision medicine.

Frontiers in immunology
BACKGROUND: Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide. Natural killer (NK) cells play a crucial role in the innate immune system and exhibit significant anti-tumor activity. However, the role of NK c...

Pharmacovigilance in the digital age: gaining insight from social media data.

Experimental biology and medicine (Maywood, N.J.)
Pharmacovigilance is essential for protecting patient health by monitoring and managing medication-related risks. Traditional methods like spontaneous reporting systems and clinical trials are valuable for identifying adverse drug events, but face de...

Type 2 Diabetes in Taiwan: Unmasking Influential Factors Through Advanced Predictive Modeling.

Journal of diabetes research
Type 2 diabetes (T2D) is influenced by lifestyle, genetics, and environmental conditions. By utilizing machine learning techniques, we can enhance the precision of T2D risk prediction by analyzing the complex interactions among these variables. This...

Hybrid optimization technique for matrix chain multiplication using Strassen's algorithm.

F1000Research
BACKGROUND: Matrix Chain Multiplication (MCM) is a fundamental problem in computational mathematics and computer science, often encountered in scientific computing, graphics, and machine learning. Traditional MCM optimization techniques use Dynamic P...

Easy and Fast Discrimination of Female Sand Flies from Species with Infrared Spectroscopy and Multivariate Analysis.

Analytical chemistry
Accurate identification of sandfly species is critical for controlling and preventing the spread of visceral leishmaniasis, a major public health concern in Latin America. Morphological similarities between female and present a significant challeng...

Leveraging pre-vaccination antibody titres across multiple influenza H3N2 variants to forecast the post-vaccination response.

EBioMedicine
BACKGROUND: Despite decades of research on the influenza virus, we still lack a predictive understanding of how vaccination reshapes each person's antibody response, which impedes efforts to design better vaccines. Models using pre-vaccination antibo...