AIMC Topic: Machine Learning

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Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges.

BMC cancer
BACKGROUND: The ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the precision oncology community. Some remarkable developments have taken place in the domain of image c...

Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.

BMC psychiatry
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattention. This study aims to develop a verifiable and interpretable machine learning model to identify ADHD...

Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.

BMC infectious diseases
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.

Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis.

BMC nephrology
OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common complications among patients undergoing maintenance hemodialysis. Early prediction of IDH and IDHTN c...

A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.

Scientific reports
Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capable of identifying early kidney disease. Kidney biopsy is unsuitable for regular screening, and imaging tests are costly and time-consuming. Several st...

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer.

Scientific reports
Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to u...

Machine learning approaches for image classification in developmental biology and clinical embryology.

Development (Cambridge, England)
The rapid increase in the amount of available biological data together with increasing computational power and innovative new machine learning algorithms has resulted in great potential for machine learning approaches to revolutionise image analysis ...

Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.

The breast journal
To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore the role of multifeature fusion. Data from 137 breast cancer postoperative patients with new nodules...

Boostering diagnosis of frontotemporal lobar degeneration with AI-driven neuroimaging - A systematic review and meta-analysis.

NeuroImage. Clinical
BACKGROUND AND OBJECTIVES: Frontotemporal lobar degeneration (FTLD) as the second most common dementia encompasses a range of syndromes and often shows overlapping symptoms with other subtypes or neurodegenerative diseases, which poses a significant ...