AIMC Topic: Case-Control Studies

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Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Cancer science
Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentia...

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

BMC endocrine disorders
BACKGROUND: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients' management. However, to our knowledge, an inst...

Deep learning prediction of attention-deficit hyperactivity disorder in African Americans by copy number variation.

Experimental biology and medicine (Maywood, N.J.)
Current understanding of the underlying molecular network and mechanism for attention-deficit hyperactivity disorder (ADHD) is lacking and incomplete. Previous studies suggest that genomic structural variations play an important role in the pathogene...

Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning.

Frontiers in immunology
Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of CO...

Construction and Validation of a Lung Cancer Diagnostic Model Based on 6-Gene Methylation Frequency in Blood, Clinical Features, and Serum Tumor Markers.

Computational and mathematical methods in medicine
Lung cancer has a high mortality rate. Promoting early diagnosis and screening of lung cancer is the most effective way to enhance the survival rate of lung cancer patients. Through computer technology, a comprehensive evaluation of genetic testing r...

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study.

PloS one
BACKGROUND: Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values ca...

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.

Computational and mathematical methods in medicine
Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary c...

Performance evaluation of a deep learning image reconstruction (DLIR) algorithm in "double low" chest CTA in children: a feasibility study.

La Radiologia medica
BACKGROUND: Chest CT angiography (CTA) is a convenient clinical examination for children with an increasing need to reduce both radiation and contrast medium doses. Iterative Reconstruction algorithms are often used to reduce image noise but encounte...

Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning.

Nature biomedical engineering
The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning...

A machine learning approach to predicting risk of myelodysplastic syndrome.

Leukemia research
BACKGROUND: Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from othe...