AIMC Topic: Machine Learning

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Machine Learning Approaches to Prognostication in Traumatic Brain Injury.

Current neurology and neuroscience reports
PURPOSE OF REVIEW: This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data-including clini...

Artificial intelligence for modelling infectious disease epidemics.

Nature
Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social sci...

Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.

Experimental biology and medicine (Maywood, N.J.)
Advancements in machine learning and deep learning have the potential to revolutionize the diagnosis of melanocytic choroidal tumors, including uveal melanoma, a potentially life-threatening eye cancer. Traditional machine learning methods rely heavi...

Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation.

Nature reviews. Cardiology
With advances in materials science and medical technology, wearable sensors have become crucial tools for the early diagnosis and continuous monitoring of numerous cardiovascular diseases, including arrhythmias, hypertension and coronary artery disea...

Prediction of early-onset bipolar using electronic health records.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical n...

IRMA: Machine learning-based harmonization of F-FDG PET brain scans in multi-center studies.

European journal of nuclear medicine and molecular imaging
PURPOSE: Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.

Unveiling the potential of Brachiaria ruziziensis: Comparative analysis of multivariate and machine learning models for biomass and NPK prediction using Vis-NIR-SWIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study investigated the development and validation of predictive models for estimating foliar nitrogen (N), phosphorus (P), and potassium (K) contents, along with shoot dry mass (SDM) of Brachiaria ruziziensis L. The approach utilized Vis-NIR-SWI...

Exploring a universal model for predicting blueberry soluble solids content based on hyperspectral imaging and transfer learning to address spatial heterogeneity challenge.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate assessment of soluble solid content (SSC) in blueberries is crucial for quality evaluation. However, in real production lines, blueberries are usually in random placement and the biological heterogeneity of blueberry parts can lead to spectr...

Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features.

Biological psychiatry. Cognitive neuroscience and neuroimaging
Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive biomarkers is essential for personalized treatment. However, most prior research studies have used ...

Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.

Cognitive, affective & behavioral neuroscience
Previous research has demonstrated that machine learning (ML) could not effectively decode passive observation of neutral versus pain photographs by using electroencephalogram (EEG) data. Consequently, the present study explored whether active viewin...