AIMC Topic: Machine Learning

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Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.

Applied clinical informatics
OBJECTIVE: The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical proce...

Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magne...

Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach.

Administration and policy in mental health
A mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a mac...

A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.

The British journal of radiology
OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stra...

Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.

Advanced materials (Deerfield Beach, Fla.)
Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can...

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning.

IEEE transactions on neural networks and learning systems
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical s...

Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images.

IEEE transactions on medical imaging
Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based i...

Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation.

IEEE transactions on medical imaging
Standard Electromechanical Wave Imaging isochrone generation relies on manual selection of zero-crossing (ZC) locations on incremental strain curves for a number of pixels in the segmented myocardium for each echocardiographic view and patient. When ...

Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface.

IEEE transactions on neural networks and learning systems
The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled envi...

Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning.

IEEE transactions on neural networks and learning systems
As a group of complex neurodevelopmental disorders, autism spectrum disorder (ASD) has been reported to have a high overall prevalence, showing an unprecedented spurt since 2000. Due to the unclear pathomechanism of ASD, it is challenging to diagnose...