The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data.

Electronic phenotyping is the task of ascertaining whether an individual has a medical condition of interest by analyzing their medical record and is foundational in clinical informatics. Increasingly, electronic phenotyping is performed via supervised learning. We investigate the effectiveness of multitask learning for phenotyping using electronic health records (EHR) data.

Algorithms to estimate PaCO2 and pH using noninvasive parameters for children with hypoxemic respiratory failure.

Background: Ventilator management for children with hypoxemic respiratory failure may benefit from ventilator protocols, which rely on blood gases. Accurate noninvasive estimates for pH or P(aCO2) could allow frequent ventilator changes to optimize lung-protective ventilation strategies. If these models are highly accurate, they can facilitate the development of closed-loop ventilator systems.