Chest Tube Management After Lung Resection Surgery using a Classifier After lung surgery, a chest tube and a pump are used to manage air leaks and fluid drainage from the chest. The decision to remove or maintain the chest tube is based on drainage data collected from a digital pump that continuously monitors the patient. We construct a classifier to support this clinical decisionmaking process by identifying patients who may suffer adverse, extended air leaks early on. Intuitively, this problem can be modelled as a timeseries fitted to monitoring data. However, we present a solution using a simple classifier constructed from data collected in a specific time frame (36 48 hours) after surgery. We hypothesize that after surgery, patients struggle to attain a stable (favourable or adverse) status which prevails after a period of discrepancies and inconsistencies in the data. A solutions, we propose, is to identify this time frame when the majority of patients achieve their states of stability. Advantages of this approach include better classification performance with a lower burden of data collection during patient treatment. The paper presents the chest tube management as a classification task performed in a sliding window over time during patient monitoring. Our results show that reliable predictions can be achieved in the time window we identify, and that our classifier reduces unsafe chest tube removals at the expense of potentially maintaining a few that can be removed, i.e., we ensure that chest tubes that need to be maintained are not removed with potentially maintaining a few unnecessarily.

An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs. While deep learning has shown promise in the domain of disease classification from medical images, models based on stateoftheart convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same hospital, but perform significantly worse when they are tested in different hospital systems. Furthermore, even within a given hospital system, deep learning models have been shown to depend on hospital and patientlevel confounders rather than meaningful pathology to make classifications. In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classification, and that they will work well even when tested on images from hospitals that were not included in the training data. We attempt to address this problem in the context of pneumonia classification from chest radiographs. We propose an approach based on adversarial optimization, which allows us to learn more robust models that do not depend on confounders. Specifically, we demonstrate improved outofhospital generalization performance of a pneumonia classifier by training a model that is invariant to the view position of chest radiographs (anteriorposterior vs. posterioranterior). Our approach leads to better predictive performance on external hospital data than both a standard baseline and previously proposed methods to handle confounding, and also suggests a method for identifying models that may rely on confounders. Code available at this https URL.