AIMC Topic: Machine Learning

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Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.

JMIR medical informatics
BACKGROUND: Missing data are a common challenge in electronic health record (EHR)-based prediction modeling. Traditional imputation methods may not suit prediction or machine learning models, and real-world use requires workflows that are implementab...

Mapping neurophysiological and molecular profiles of heterogeneity and homogeneity in schizophrenia-bipolar disorder.

Science advances
The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connec...

Applied machine learning for nociceptive pain detection using EEG spectral features.

Biomedical physics & engineering express
. This study explores a more reliable method for measuring nociceptive pain induced by laser stimuli from electroencephalography (EEG) signals, addressing the limitations of fixed pain scales by incorporating inter-individual variability in subjectiv...

Predicting 30-day and 1-year mortality in heart failure with preserved ejection fraction (HFpEF).

PloS one
OBJECTIVES: To develop and compare prediction models for 30-day and 1-year mortality in Heart failure with preserved ejection fraction (HFpEF) using EHR data, utilizing both traditional and machine learning (ML) techniques.

From wastewater to epidemiological insights: A systematic review of modeling strategies for infectious disease surveillance.

Water research
Wastewater-based epidemiology (WBE) has emerged as a promising complementary tool in infectious diseases surveillance systems, offering real-time insights into the disease dynamics across various spatial coverage. By leveraging wastewater data, a wid...

Predictive surveillance and diagnosis of COVID-19: An integrative machine learning and wastewater multi-omics approach.

Water research
COVID-19 has had major global impacts, highlighting the importance of robust predictive surveillance and diagnostic systems to ensure effective public health responses. Traditional surveillance methods based on passive case counting and diagnostic te...

Research on the Identification of Composite Spices Based on Terahertz Spectroscopy and Machine Learning Algorithms.

Journal of food protection
The similar appearance and composition of pungent spices frequently give rise to adulteration, which not only causes market confusion but also results in inconsistent product quality. This study employed terahertz time-domain spectra and absorption s...

Green Bond Issuance and Carbon Emissions: Can Causal Machine Learning Inform Forward-Looking Policy Decisions?

Environmental science & technology
Green bonds finance projects intended to deliver environmental benefits, including reductions in greenhouse gas emissions. However, evidence that municipal green bond issuance lowers local carbon emissions remains limited and lacks the spatial and te...

Enhancing Permeability Prediction of Heterobifunctional Degraders Using Machine Learning and Metadynamics-Informed 3D Molecular Descriptors.

Journal of chemical information and modeling
Heterobifunctional degraders, a class of targeted protein degraders (TPDs), often occupy beyond-rule-of-five (bRo5) chemical space, where traditional passive permeability models─calibrated on drug-like molecules or peptides and based on topological d...

Label-free histological identification of intraductal carcinoma of the prostate using texture analysis-based multimodal stimulated Raman scattering microscopy.

Scientific reports
Intraductal carcinoma of the prostate (IDC-P) is a very aggressive histopathological subtype of prostate cancer (PCa) that is strongly associated with poor clinical outcomes but for which no accurate biomarkers exist. Here, we demonstrate a novel app...