A Novel Panel of Plasma Proteins Predicts Progression in Prodromal Alzheimer's Disease.

Background: A cheap and minimum-invasive method for early identification of Alzheimer's disease (AD) pathogenesis is key to disease management and the success of emerging treatments targeting the prodromal phases of the disease.

Objective: To develop a machine learning-based blood panel to predict the progression from mild cognitive impairment (MCI) to dementia due to AD within a four-year time-to-conversion horizon.

Methods: We created over one billion models to predict the probability of conversion from MCI to dementia due to AD and chose the best-performing one.

Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations.

The aim of this study is to build machine learning models to predict severe complications using administrative and clinical elements that are collected immediately after patient admission to the intensive care unit (ICU). Risk models are of increasing importance in the ICU setting. However, they generally present the black-box issue because they do not provide meaningful information about the logic involved in patient-specific predictions.

Explainable death toll motion modeling: COVID-19 data-driven narratives.

Models have gained the spotlight in many discussions surrounding COVID-19. The urgency for timely decisions resulted in a multitude of models as informed policy actions must be made even when so many uncertainties about the pandemic still remain. In this paper, we use machine learning algorithms to build intuitive country-level COVID-19 motion models described by death toll velocity and acceleration.