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MedXY AI/MedXY News/Tag: phenotyping
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Quantitative CT-Based Mixed Mucus Plug-Emphysema Phenotyping Predicts Exacerbation Risk in COPD: Integrating Imaging Biomarkers for Precision Medicine
Clinical UpdatesCOPDacute exacerbation

Quantitative CT-Based Mixed Mucus Plug-Emphysema Phenotyping Predicts Exacerbation Risk in COPD: Integrating Imaging Biomarkers for Precision Medicine

By MedXY|Sep 24, 2026

This review synthesizes recent evidence on quantitative CT markers of mucus plugging and emphysema in COPD, highlighting their combined impact on acute exacerbation risk and clinical heterogeneity, and discusses implications for personalize

Harnessing Precision Medicine: The APS Consortium’s National Platform for Phenotyping ARDS, Pneumonia, and Sepsis
Critical Care
ARDS
critical care

Harnessing Precision Medicine: The APS Consortium’s National Platform for Phenotyping ARDS, Pneumonia, and Sepsis

By MedXY|Jul 15, 2026

The APS Consortium establishes a large, prospective cohort of critically ill adults with ARDS, pneumonia, and sepsis to enable biologically based phenotyping, leveraging rich clinical data and biospecimens for improved understanding and the

Decoding Pulmonary Arterial Hypertension in Congenital Heart Disease: Latent Class Analysis Uncovers Distinct Risk Phenotypes and Treatment Insights
Cardiologycongenital heart diseasephenotyping

Decoding Pulmonary Arterial Hypertension in Congenital Heart Disease: Latent Class Analysis Uncovers Distinct Risk Phenotypes and Treatment Insights

By MedXY|Jun 25, 2026

Latent class analysis of 889 adult PAH-CHD patients identified unique phenogroups with distinct survival outcomes and treatment responses, offering refined risk stratification and guiding personalized therapy.

Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study
AIpreeclampsiaObstetrics

Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study

By MedXY|Aug 18, 2026

Unsupervised machine learning identified three distinct preeclampsia phenotypes linked to timing of delivery, placental dysfunction, fetal growth, and complications, suggesting a path toward more personalized risk assessment and management.

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