This research investigates how the choice of time interval affects reinforcement learning models for sepsis treatment in intensive care. By comparing one-, two-, four-, and eight-hour patient timelines, it demonstrates that shorter intervals better capture patient dynamics, improve estimated survival outcomes, and may enable more effective AI-assisted clinical decision-making.

Acute respiratory distress syndrome (ARDS) causes severe breathing failure and kills tens of thousands annually, yet has no effective treatment. This research studies how ARDS disrupts lung surfactant, a critical stabilizing substance in the lungs. By identifying immune-related factors that damage surfactant, the work aims to develop the first targeted therapeutic cure.