Smart Imitator: Learning from Imperfect Clinical Decisions.

Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.

Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.

High temperature and humidity storage alter starch properties of faba (Vicia faba) and adzuki beans (Vigna angularis) associated with hard-to-cook quality.

Hard-to-cook (HTC) beans are characterised by extended cooking times. Although the changes in cell walls limiting hydration in HTC beans are widely investigated, the role of macro-molecules (starch and protein, which constitute >80 % of beans) are almost overlooked. This study investigates the structural changes in starch associated with the HTC quality in faba and adzuki beans stored at contrasting temperature and humidity regimes.

Qualitative American Heart Association plot of late gadolinium enhancement with mortality and ventricular arrhythmia prediction using artificial intelligence.

Background: The prognostic value of late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) imaging is well-established. However, the direct relationship between image pixels and outcomes remains poorly understood. We hypothesised that leveraging artificial intelligence (AI) to analyse qualitative LGE images based on American Heart Association (AHA) guidelines could elucidate this relationship.