AIMC Topic: Machine Learning

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Development and validation of a machine learning model to predict moderate-to-severe cancer-related fatigue in breast cancer.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This study aimed to establish and validate a machine learning model for predicting moderate-to-severe cancer-related fatigue (CRF) 2 years after completion of anti-tumor therapy in breast cancer patients.

Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance.

PLoS biology
Antimicrobial resistance (AMR) poses a growing threat to human health. Increasingly, genome sequencing is being applied for the surveillance of bacterial pathogens, producing a wealth of data to train machine learning (ML) applications to predict AMR...

SEANN: A domain-informed neural network for epidemiological insights.

PloS one
In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine lea...

Solvent-Inclusive ML/MM Simulations: Assessments of Structural, Dynamical, and Thermodynamic Accuracy.

Journal of chemical information and modeling
Chemical reactions in solution are central to biological function, synthetic chemistry, and materials design. Accurate modeling of these systems is essential for obtaining mechanistic insights but remains computationally demanding. Hybrid machine-lea...

Exploring the mechanism of autophagy and inflammation in osteoarthritis through bioinformatics and experimental verification.

Autoimmunity
Osteoarthritis (OA) is a chronic joint disorder characterized by cartilage degradation and accompanied by inflammatory processes. Inflammation and autophagy dysfunction have been associated with the pathogenesis of OA. However, the interplay between ...

Machine learning-based mortality risk prediction model for elderly diabetic patients with non-ST-segment elevation myocardial infarction using MIMIC-IV database.

Scientific reports
Non-ST-elevation myocardial infarction (NSTEMI) in elderly diabetic patients presents unique challenges in risk assessment and prognosis prediction. This study aimed to develop and validate a machine learning-based mortality risk prediction model for...

Early prediction of vasopressor initiation in ICU sepsis patients using an interpretable EHR-based ML model.

BMC medical informatics and decision making
BACKGROUND: Early identification of septic patients who will require vasopressor support could provide a critical window for hemodynamic optimisation, yet current bedside cues often appear only when shock is imminent.

An open-source screening platform accelerates discovery of drug combinations.

Nature communications
Drug combinations are essential to modern medicine, but their discovery remains slow and inefficient as experimental complexity expands rapidly with each additional drug tested. Although modern liquid handling systems enable complex and highly custom...

Multimodal Data-Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure With Preserved Ejection Fraction: Multicenter, Cross-Regional Cohort Study.

Journal of medical Internet research
BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) and unstable angina (UA) often coexist in clinical practice, constituting a high-risk cardiovascular phenotype with a markedly increased incidence of major adverse cardiovascular even...

Harmful epistemic dependence on medical machine learning and its moral implications.

Journal of medical ethics
The advances in machine learning (ML)-based systems in medicine give rise to pressing epistemological and ethical questions. Clinical decisions are increasingly taken in highly digitised work environments, which we call artificial epistemic niches. B...