Interpretable Machine Learning Model for Fungal Infection Prediction: A Real‐World Study
Our study successfully developed and validated a machine learning model based on large‐scale electronic health record data to predict the risk of fungal infections in hospitalized patients. Through a Streamlit‐powered web platform, the model was seamlessly embedded into clinical workflows, completing the full pipeline from model construction to real ...
Jinru Yang +5 more
wiley +1 more source
Development of a Machine-Learning Immuno-Serologic Diagnostic Model for Non-Neutropenic Invasive Pulmonary Fungal Disease. [PDF]
Huang H, Fang F, Lu W, Liu Z, Huang J.
europepmc +1 more source
ABSTRACT Sub‐Saharan Africa (SSA) faces a unique cancer burden, characterized by a disproportionately high mortality‐to‐incidence ratio, a large share of infection‐related cancers, and distinct environmental exposures. Implementation of national policies on primary prevention is limited, and public literacy regarding cancer risk and curability remains ...
Anthony Kityo +14 more
wiley +1 more source
Linking Soil Microbial Functional Profiles to Fungal Disease Resistance in Winter Barley Under Different Fertilisation Regimes. [PDF]
Petkova M, Chavdarov P, Shilev S.
europepmc +1 more source
Integrating beneficial microorganisms and soil amendment for grapevine health: toward eco-friendly seasonal fungal disease management and soil improvement. [PDF]
Hajji-Hedfi L +5 more
europepmc +1 more source
Invasive Fungal Disease in Solid Organ and Hematopoietic Cell Transplant Recipients, United States. [PDF]
Gold JAW +7 more
europepmc +1 more source
Cracks in the Curriculum: The Hidden Deficiencies in Fungal Disease Coverage in Medical Books. [PDF]
Rost IH +9 more
europepmc +1 more source
The Changing Epidemiology of Breakthrough Invasive Fungal Disease in Allogeneic Hematopoietic Stem Cell Transplant Recipients in the Era of Modified-Release Posaconazole Prophylaxis. [PDF]
Tio SY +7 more
europepmc +1 more source

