Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

Cardiac patients undergoing surgery face increased risk of postoperative complications, due to a combination of factors, including higher risk surgery, their age at time of surgery and the presence of co-morbid conditions. They will therefore require high levels of care and clinical resources throughout their perioperative journey (i.e. before, during and after surgery). Although surgical mortality rates in the UK have remained low, postoperative complications on the other hand are common and can have a significant impact on patients’ quality of life, increase hospital length of stay and healthcare costs. In this study we used and compared several machine learning methods – random forest, AdaBoost, gradient boosting model and stacking – to predict severe postoperative complications after cardiac surgery based on preoperative variables obtained from a surgical database of a large acute care hospital in Scotland. Our results show that AdaBoost has the best overall performance (AUC = 0.731), and also outperforms EuroSCORE and EuroSCORE II in other studies predicting postoperative complications. Random forest (Sensitivity = 0.852, negative predictive value = 0.923), however, and gradient boosting model (Sensitivity = 0.875 and negative predictive value = 0.920) have the best performance at predicting severe postoperative complications based on sensitivity and negative predictive value

Evaluation of random forest and ensemble methods at predicting complications following cardiac surgery

Cardiac patients undergoing surgery face increased risk of postoperative complications, due to a combination of factors, including higher risk surgery, their age at time of surgery and the presence of co-morbid conditions. They will therefore require high levels of care and clinical resources throughout their perioperative journey (i.e. before, during and after surgery). Although surgical mortality rates in the UK have remained low, postoperative complications on the other hand are common and can have a significant impact on patients’ quality of life, increase hospital length of stay and healthcare costs. In this study we used and compared several machine learning methods – random forest, AdaBoost, gradient boosting model and stacking – to predict severe postoperative complications after cardiac surgery based on preoperative variables obtained from a surgical database of a large acute care hospital in Scotland. Our results show that AdaBoost has the best overall performance (AUC = 0.731), and also outperforms EuroSCORE and EuroSCORE II in other studies predicting postoperative complications. Random forest (Sensitivity = 0.852, negative predictive value = 0.923), however, and gradient boosting model (Sensitivity = 0.875 and negative predictive value = 0.920) have the best performance at predicting severe postoperative complications based on sensitivity and negative predictive value

MMS observation of asymmetric reconnection supported by 3-D electron pressure divergence

We identify the electron diffusion region (EDR) of a guide field dayside reconnection site encountered by the Magnetospheric Multiscale (MMS) mission and estimate the terms in generalized Ohm's law that controlled energy conversion near the X-point. MMS crossed the moderate-shear (∼130°) magnetopause southward of the exact X-point. MMS likely entered the magnetopause far from the X-point, outside the EDR, as the size of the reconnection layer was less than but comparable to the magnetosheath proton gyroradius, and also as anisotropic gyrotropic "outflow" crescent electron distributions were observed. MMS then approached the X-point, where all four spacecraft simultaneously observed signatures of the EDR, for example, an intense out-of-plane electron current, moderate electron agyrotropy, intense electron anisotropy, nonideal electric fields, and nonideal energy conversion. We find that the electric field associated with the nonideal energy conversion is (a) well described by the sum of the electron inertial and pressure divergence terms in generalized Ohms law though (b) the pressure divergence term dominates the inertial term by roughly a factor of 5:1, (c) both the gyrotropic and agyrotropic pressure forces contribute to energy conversion at the X-point, and (d) both out-of-the-reconnection-plane gradients (∂/∂M) and in-plane (∂/∂L,N) in the pressure tensor contribute to energy conversion near the X-point. This indicates that this EDR had some electron-scale structure in the out-of-plane direction during the time when (and at the location where) the reconnection site was observed