AIMC Topic: Machine Learning

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Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.

BMJ open
OBJECTIVES: Methotrexate (MTX) effectively controls rheumatoid arthritis (RA) but often leads to side effects (SE) such as gastrointestinal (GI) issues, liver toxicity and bone marrow suppression. To develop clinically interpretable machine learning ...

Ecology-informed symbolic machine learning: a methodological framework for classification of forest succession.

Environmental monitoring and assessment
Accurately classifying forest successional stages remains a major challenge in applied ecology due to the continuum of succession, ecological heterogeneity, and limited interpretability of many machine learning (ML) approaches. Prevailing models typi...

DyeSPY: Establishing the First Forensic SERS Reference for Hair Dye Colorant Evidence.

Analytical chemistry
Hair dyeing is a widespread practice with potential forensic value in individual identification, yet most analytical approaches are destructive, time-intensive, or lack sensitivity for trace residues. Surface-enhanced Raman spectroscopy (SERS) offers...

Exploring the impact of endocrine-disrupting chemicals on erectile dysfunction through network toxicology and machine learning.

BMC pharmacology & toxicology
BACKGROUND: Erectile dysfunction (ED) is a common male sexual disorder with a multifactorial etiology. The exposure to endocrine-disrupting chemicals (EDCs) has been increasingly linked to reproductive health disorders in both men and women. EDCs can...

Interpretable machine learning model based on multimodal ultrasound for bedside diagnosis of acute exacerbations in COPD.

Respiratory research
BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with accelerated lung function decline and increased mortality. However, early and accurate diagnosis remains clinically challenging due to nonspecific s...

Large language models versus classical machine learning performance in COVID-19 mortality prediction using high-dimensional tabular data.

Scientific reports
This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML m...

Predictive biomarkers validation of CD3 cell apheresis yield in CAR-T manufacturing for diffuse large B-cell lymphoma: a machine learning approach.

Scientific reports
Chimeric antigen receptor (CAR) T-cell therapy has shown significant success in treating diffuse large B-cell lymphoma (DLBCL). The initial step involves collecting autologous CD3 lymphocytes through apheresis, in which obtaining an adequate CD3 cell...

Survival machine learning models for predicting all-cause and case-specific mortality risk in metabolic dysfunction-associated fatty liver disease patients.

Scientific reports
Emerging evidence links metabolic dysfunction-associated fatty liver disease (MAFLD) with increased all-cause and circulatory system disease (CSD) mortality in adults, yet survival machine learning studies are limited. This study analyzed 4415 NHANES...

Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.

Scientific reports
Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a fee...

Development and validation of an interpretable predictive machine learning model for successful weaning of continuous renal replacement therapy.

Scientific reports
Continuous renal replacement therapy (CRRT) is a vital intervention for critically ill patients with severe acute kidney injury, yet no standardized criteria exist to determine the optimal time for its discontinuation. We developed and validated mach...