AIMC Topic: Algorithms

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Retrospective analysis of interpretable machine learning in predicting ICU thrombocytopenia in geriatric ICU patients.

Scientific reports
We developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients during their stay in the intensive care unit (ICU), ultimately aiding clinical decision-making and impr...

Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment.

Environmental science and pollution research international
Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptati...

Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human.

Journal of bioinformatics and computational biology
Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it challenging to predict a drug's potential effects on an organism's overall metabolism. The mapping of dr...

Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.

Skeletal radiology
OBJECTIVES: This study utilizes [Tc]-methylene diphosphate (MDP) single photon emission computed tomography (SPECT) images as a reference standard to evaluate whether the integration of radiomics features from computed tomography (CT) and machine lea...

Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder.

Magnetic resonance in medicine
PURPOSE: To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods.

Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.

Medical & biological engineering & computing
Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic sys...

A self-supervised embedding of cell migration features for behavior discovery over cell populations.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recent studies point out that the dynamics and interaction of cell populations within their environment are related to several biological processes in immunology. Hence, single-cell analysis in immunology now relies on spati...

Artificial Intelligence-Based Histopathological Subtyping of High-Grade Serous Ovarian Cancer.

The American journal of pathology
Four subtypes of ovarian high-grade serous carcinoma (HGSC) have previously been identified, each with different prognoses and drug sensitivities. However, the accuracy of classification depended on the assessor's experience. This study aimed to deve...

Navigating the future of health care with AI-driven digital therapeutics.

Drug discovery today
Digital therapeutics (DTx) is a recently conceived idea in health care that aims to cure ailments and modify patient behavior by employing a range of digital technologies. Notably, when traditional medication is not entirely efficacious, DTx offers a...

Enhancing identification performance of cognitive impairment high-risk based on a semi-supervised learning method.

Journal of biomedical informatics
BACKGROUND: Cognitive assessment plays a pivotal role in the early detection of cognitive impairment, particularly in the prevention and management of cognitive diseases such as Alzheimer's and Lewy body dementia. Large-scale screening relies heavily...