AIMC Topic: Machine Learning

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Enhancing the classification of isolated theropod teeth using machine learning: a comparative study.

PeerJ
Classifying objects, such as taxonomic identification of fossils based on morphometric variables, is a time-consuming process. This task is further complicated by intra-class variability, which makes it ideal for automation via machine learning (ML) ...

Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

PloS one
Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permanent disability and stigma. Described as a disease of poverty, the impact of socioeconomic indicators ...

Non-invasive estimation of beat-by-beat aortic blood pressures from electrical impedance tomography data processed by machine learning.

Journal of clinical monitoring and computing
Hypotension in perioperative and intensive care settings is a significant risk factor associated with complications such as myocardial infarction and kidney injury thereby increasing perioperative complications and mortality. Continuous blood pressur...

IBPL: Information Bottleneck-based Prompt Learning for graph out-of-distribution detection.

Neural networks : the official journal of the International Neural Network Society
When training and test graph samples follow different data distributions, graph out-of-distribution (OOD) detection becomes an indispensable component of constructing the reliable and safe graph learning systems. Motivated by the significant progress...

Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis.

Academic radiology
BACKGROUND: Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.

Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload.

Academic radiology
RATIONALE AND OBJECTIVE: Clinical workload can fluctuate daily in radiology practice. We sought to design, validate, and implement an efficient and sustainable machine learning model to forecast daily clinical image interpretation workload.

Knowledge Graph-Based Few-Shot Learning for Label of Medical Imaging Reports.

Academic radiology
BACKGROUND: The application of artificial intelligence (AI) in the field of automatic imaging report labeling faces the challenge of manually labeling large datasets.

Machine Learning Analysis of Videourodynamics to Predict Incident Hydronephrosis in Patients With Spina Bifida.

The Journal of urology
PURPOSE: Variability in the interpretation of videourodynamics studies limits reliable classification of kidney injury risk for patients with spina bifida. We developed machine learning models to predict incident hydronephrosis in patients with spina...

Who Tweets for the autistic community? A natural language processing-driven investigation.

Autism : the international journal of research and practice
The formation of autism advocacy organisations led by family members of autistic individuals led to intense criticism from some parts of the autistic community. In response to what was perceived as a misrepresentation of their interests, autistic ind...

Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting.

Clinica chimica acta; international journal of clinical chemistry
A standard for reporting genetic pathology results currently does not exist as a consensus. While effective reports are produced, there is lack of consistency on which details to present or to emphasise, and the ultimate report often reflects an indi...