AIMC Topic: Machine Learning

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The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.

JMIR medical informatics
BACKGROUND: Machine learning (ML) and big data analytics are rapidly transforming health care, particularly disease prediction, management, and personalized care. With the increasing availability of real-world data (RWD) from diverse sources, such as...

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study.

JMIR cancer
BACKGROUND: Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective preven...

Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.

JMIR infodemiology
BACKGROUND: The opioid crisis poses a significant health challenge in the United States, with increasing overdoses and death rates due to opioids mixed with other illicit substances. Various strategies have been developed by federal and local governm...

WISP2/CCN5 revealed as a potential diagnostic biomarker for endometriosis based on machine learning and single-cell transcriptomic analysis.

Functional & integrative genomics
OBJECTIVE: Endometriosis is a prevalent gynecological disease characterized by the ectopic growth of functional endometrial tissue outside the uterine cavity, affecting millions of women worldwide. Currently, the definitive diagnosis relies on invasi...

Optimizing beat-wise input for arrhythmia detection using 1-D convolutional neural networks: A real-world ECG study.

Computer methods and programs in biomedicine
BACKGROUNDS AND OBJECTIVES: Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise class...

Multi-datasets transfer multitask learning for simultaneous blood glucose and blood pressure monitoring using common PPG features.

Computers in biology and medicine
The simultaneous monitoring of both blood glucose level (BGL) and blood pressure (BP) has rarely been studied directly. The exploitation of physiological interactions between them will advance the learning of either task. However, the lack of availab...

Interactive prototype learning and self-learning for few-shot medical image segmentation.

Artificial intelligence in medicine
Few-shot learning alleviates the heavy dependence of medical image segmentation on large-scale labeled data, but it shows strong performance gaps when dealing with new tasks compared with traditional deep learning. Existing methods mainly learn the c...

Radiogenomic insights suggest that multiscale tumor heterogeneity is associated with interpretable radiomic features and outcomes in cancer patients.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: To develop radiogenomic subtypes and determine the relationships between radiomic phenotypes and multiomics molecular characteristics.

Machine learning to predict mitochondrial diseases by phenotypes.

Mitochondrion
Diagnosing mitochondrial diseases remains challenging because of the heterogeneous symptoms. This study aims to use machine learning to predict mitochondrial diseases from phenotypes to reduce genetic testing costs. This study included patients who u...

Shared and distinct neural signatures of feature and spatial attention.

NeuroImage
The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent act...