AIMC Topic: Machine Learning

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Machine learning models for classifying non-specific neck pain using craniocervical posture and movement.

Musculoskeletal science & practice
OBJECTIVE: Physical therapists and clinicians commonly confirm craniocervical posture (CCP), cervical retraction, and craniocervical flexion as screening tests because they contribute to non-specific neck pain (NSNP). We compared the predictive perfo...

SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations.

Computational biology and chemistry
The computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predi...

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Antimicrobial agents and chemotherapy
Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comp...

[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening].

Bulletin du cancer
INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of a...

A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.

Statistical methods in medical research
Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history....

Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.

Arthritis care & research
OBJECTIVE: One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which...

Inter-reviewer reliability of human literature reviewing and implications for the introduction of machine-assisted systematic reviews: a mixed-methods review.

BMJ open
OBJECTIVES: Our main objective is to assess the inter-reviewer reliability (IRR) reported in published systematic literature reviews (SLRs). Our secondary objective is to determine the expected IRR by authors of SLRs for both human and machine-assist...

A visual-language foundation model for computational pathology.

Nature medicine
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult d...

Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: In this study, we developed a machine learning approach for postoperative corneal endothelial cell images of patients who underwent Descemet's membrane keratoplasty (DMEK).

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.

GeroScience
Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing...