AIMC Topic: Machine Learning

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Health-related quality of life among healthcare workers: a comparative analysis using regression, conditional tree and forests.

BMC public health
BACKGROUND: Considering the potential importance of health care workers (HCWs) in maintaining and improving the health of society, we decided to investigate the factors affecting the health-related quality of life (HRQoL) of HCWs using machine learni...

Unraveling diethyl phthalate-induced prostate carcinogenesis: core targets revealed by integrated network toxicology, machine learning, and structural validation.

Human genomics
PURPOSE: Diethyl phthalate (DEP), a widely distributed environmental contaminant, is epidemiologically linked to prostate cancer (PCa). However, its molecular mechanisms beyond endocrine disruption remain poorly defined. We aimed to investigate the c...

Prediction and analysis of anti-aging peptides using data augmentation and machine learning algorithms.

BMC biology
BACKGROUND: For most species, Aging is an inevitable biological process that poses significant challenges to global healthcare due to age-related diseases. Recent advances in peptide therapy have highlighted anti-aging peptides (AAPs) as a promising ...

Harnessing hyperspectral imaging and machine learning techniques for accurate discrimination of peanut plants and weeds.

Scientific reports
Effective weed detection for precise management remains a pertinent issue in modern agriculture. In this study, hyperspectral imaging (HSI) was combined with machine learning (ML) to differentiate between peanut plants and four common weeds found in ...

Effects of bisphosphonates after denosumab discontinuation and treatment effect heterogeneity using causal machine learning.

Scientific reports
Discontinuation of denosumab is associated with a rebound increase in osteoporotic fracture (OF) risk, and bisphosphonates (BPs) are commonly recommended as sequential therapy to mitigate this risk. However, their real-world effectiveness-and whether...

Analyzing the impact of the automatic ball strike system in professional baseball through a case study on KBO league data.

Scientific reports
Recent advancements in professional baseball have led to the introduction of the Automated Ball-Strike (ABS) system, or "robot umpires," which utilize machine learning, computer vision, and precise tracking technologies to automate ball-strike calls....

Unraveling tissue-specific molecular targets of dihydroartemisinin in non-small cell lung cancer: an integrative machine learning and network pharmacology approach.

Medical oncology (Northwood, London, England)
Non-small cell lung cancer (NSCLC) presents significant therapeutic challenges due to resistance and immune evasion. Dihydroartemisinin (DHA), a derivative of artemisinin, exhibits broad anti-tumor activity, but its molecular targets and mechanisms i...

Early detection of paroxysmal atrial fibrillation from non-episodic ECG data using cardiac dynamics features and different classification models.

Biomedical physics & engineering express
Intelligent computer-aided diagnosis techniques enable inspection of invisible electrocardiogram (ECG) pathological changes for early detection of latent heart diseases. This study concentrates on latent pathological changes within non-episodic ECG d...

Design of Highly Potent Antibiofilm, Antimicrobial Peptides Using Explainable Artificial Intelligence.

Journal of chemical information and modeling
Antimicrobial peptides have emerged as a potential alternative to traditional small-molecule antimicrobials. They possess broad-spectrum efficacy and increasingly confront the challenges of bacterial resistance, especially the adaptive resistance of ...

Establishment of a postoperative delirium risk prediction model for elderly hip fracture patients based on machine learning algorithms.

BMC geriatrics
BACKGROUND: Although no definitive treatment exists, 30-40% of postoperative delirium cases are preventable through early risk identification and intervention. Therefore our aim was to develop and evaluate a postoperative delirium risk prediction mod...