A machine-learning approach to predicting hypotensive events in ICU settings.

Background: Predicting hypotension well in advance provides physicians with enough time to respond with proper therapeutic measures. However, the real-time prediction of hypotension with high positive predictive value (PPV) is a challenge. This is due to the dynamic changes in patients' physiological status following drug administration, which limits the quantity of useful data available for the algorithm.

Shape optimization of stress concentration-free lattice for self-expandable Nitinol stent-grafts.

In a mechanical component, stress-concentration is one of the factors contributing to reduce fatigue life. This paper presents a design methodology based on shape optimization to improve the fatigue safety factor and increase the radial stiffness of Nitinol self-expandable stent-grafts. A planar lattice free of stress concentrators is proposed for the synthesis of a stent with smooth cell shapes.