Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi.

Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in understanding health outcomes. In this paper, we showcase the use of machine-learned staged tree models for investigating complex asymmetric dependence structures in health data. Staged trees are a specific class of generative, probabilistic graphical models that formally model asymmetric conditional independence and non-regular sample spaces.

Skin reaction with Eosinophilia and Systemic Symptoms after lenalidomide in peritoneal Dialysis.

The Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) syndrome is a fatal and immune-mediated idiosyncratic drug reaction, with symptoms of fever, skin eruptions (that involves more than half of the body surface), facial oedema and hematological disorders, all presenting within the latent period following drug intake. Effects can also be seen on multiple organs, most notably hepatitis in liver and acute interstitial nephritis in kidney, generally post-administration of allopurinol. The European Registry of Severe Cutaneous Adverse Reactions (RegiSCAR) classifies the DRESS Syndrome cases as "definite", "probable" or "possible", based on clinical and laboratory features.

Automated Prediction of Kidney Failure in IgA Nephropathy with Deep Learning from Biopsy Images.

Background And Objectives: Digital pathology and artificial intelligence offer new opportunities for automatic histologic scoring. We applied a deep learning approach to IgA nephropathy biopsy images to develop an automatic histologic prognostic score, assessed against ground truth (kidney failure) among patients with IgA nephropathy who were treated over 39 years. We assessed noninferiority in comparison with the histologic component of currently validated predictive tools.

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks.

Background And Objectives: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting.