AIMC Topic: Machine Learning

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Detection of anemic condition in patients from clinical markers and explainable artificial intelligence.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Anaemia is a commonly known blood illness worldwide. Red blood cell (RBC) count or oxygen carrying capability being insufficient are two ways to describe anaemia. This disorder has an impact on the quality of life. If anaemia is detected ...

Heterocyclic-Based Analogues against Sarcine-Ricin Loop RNA from : Molecular Docking Study and Machine Learning Classifiers.

Medicinal chemistry (Shariqah (United Arab Emirates))
BACKGROUND: Heterocyclic-based drugs have strong bioactivities, are active pharmacophores, and are used to design several antibacterial drugs. Due to the diverse biodynamic properties of well-known heterocyclic cores, such as quinoline, indole, and i...

A Strategy based on Bioinformatics and Machine Learning Algorithms Reveals Potential Mechanisms of Shelian Capsule against Hepatocellular Carcinoma.

Current pharmaceutical design
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening form of cancer, with Shelian Capsule (SLC), a traditional Chinese medicine (TCM) formulation, being recommended for clinical treatment. However, the mechanisms underlying ...

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography.

Journal of X-ray science and technology
PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram.

Development and validation of a clinical prediction model for glioma grade using machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.

Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator.

Chaos (Woodbury, N.Y.)
We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI ...

Machine Learning to Predict Teratogenicity: Theory and Practice.

Methods in molecular biology (Clifton, N.J.)
Machine learning (ML) is a subfield of artificial intelligence (AI) that consists of developing algorithms that can automatically learn patterns and relationships from data, without being explicitly programmed. It continues to advance with the develo...

Coronary heart disease prediction based on hybrid deep learning.

The Review of scientific instruments
Machine learning provides increasingly reliable assistance for medical experts in diagnosing coronary heart disease. This study proposes a deep learning hybrid model based coronary heart disease (CAD) prediction method, which can significantly improv...

Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.

Radiology
Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a s...

Application of Machine-learning based on Radiomics Features in Differential Diagnosis of Superficial Lymphadenopathy.

Current medical imaging
OBJECTIVE: The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes.