AIMC Topic: Machine Learning

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Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer.

Journal of translational medicine
BACKGROUND: Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent advancements in its treatment, the prognosis for patients with GC remains poor. Understanding the mechanisms of cell death in GC, particularly those r...

Using machine learning to develop a five-item short form of the children's depression inventory.

BMC public health
BACKGROUND: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is...

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning.

BMC cancer
BACKGROUND: Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.

Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.

Scientific reports
Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standa...

Machine-learning prediction of a novel diagnostic model using mitochondria-related genes for patients with bladder cancer.

Scientific reports
Bladder cancer (BC) is the ninth most-common cancer worldwide and it is associated with high morbidity and mortality. Mitochondrial Dysfunction is involved in the progression of BC. This study aimed to developed a novel diagnostic model based on mito...

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

JMIR research protocols
BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and m...

[The revolution of AI in drug development].

Medecine sciences : M/S
Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to repre...

Application of Artificial Intelligence in Tissue Engineering.

Tissue engineering. Part B, Reviews
Tissue engineering, a crucial approach in medical research and clinical applications, aims to regenerate damaged organs. By combining stem cells, biochemical factors, and biomaterials, it encounters challenges in designing complex 3D structures. Arti...

The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis.

Cardiology
INTRODUCTION: Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to system...