AIMC Topic: Algorithms

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A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children.

Pediatric surgery international
INTRODUCTION: There is a tendency toward nonoperative management of appendicitis resulting in an increasing need for preoperative diagnosis and classification. For medical purposes, simple conceptual decision-making models that can learn are widely u...

End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism.

Sensors (Basel, Switzerland)
Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse trans...

Time-of-flight secondary ion mass spectrometry analysis of hair samples using unsupervised artificial neural network.

Biointerphases
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is extensively employed for the structural analysis of the outermost surfaces of organic materials, including biological materials, because it provides detailed compositional information and e...

4D-CT deformable image registration using multiscale unsupervised deep learning.

Physics in medicine and biology
Deformable image registration (DIR) of 4D-CT images is important in multiple radiation therapy applications including motion tracking of soft tissue or fiducial markers, target definition, image fusion, dose accumulation and treatment response evalua...

Development of an artificial intelligence system using deep learning to indicate anatomical landmarks during laparoscopic cholecystectomy.

Surgical endoscopy
BACKGROUND: The occurrence of bile duct injury (BDI) during laparoscopic cholecystectomy (LC) is an important medical issue. Expert surgeons prevent intraoperative BDI by identifying four landmarks. The present study aimed to develop a system that ou...

A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples.

International journal of cardiology
BACKGROUND: Deep learning (DL) has shown promising results in improving atrial fibrillation (AF) detection algorithms. However, these models are often criticized because of their "black box" nature.

The Synthesizability of Molecules Proposed by Generative Models.

Journal of chemical information and modeling
The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early stage drug discovery is molecular genera...

Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups.

Genes
Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes f...

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms.

International journal of environmental research and public health
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susce...

Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment.

Scientific reports
Human learning is one of the main topics in psychology and cognitive neuroscience. The analysis of experimental data, e.g. from category learning experiments, is a major challenge due to confounding factors related to perceptual processing, feedback ...