An Epidemiological Study of Anemia and Renal Dysfunction in Patients Admitted to ICUs across the United States

The aims of this study were to determine the associations between anemia of critical illness, erythropoietin stimulating agents (ESA), packed red blood cell transfusions and varying degrees of renal dysfunction with mortality, and ICU- and hospital length of stay (LOS). This was a cross-sectional retrospective study of 5,314 ICU patients from USA hospitals. Hospital, patient demographics, and clinical characteristics were collected. Predictors of mortality and hospital and ICU LOS were evaluated using multivariate logistic regression models. The mean ICU admission hemoglobin in this study was 9.4 g/dL. The prevalence of ESA use was 13% and was associated with declining renal function; 26% of the ICU patients in this study received transfusion. ESA utilization was associated with 28% longer hospital LOS (). ICU LOS was increased by up to 18% in patients with eGFR rates of \u3c30 and 30–59 mL/min/1.73 m2, respectively () but not in those receiving dialysis. Mortality was significantly associated with renal dysfunction and dialysis with odds ratios of 1.94, 2.66 and 1.40 for the dialysis, and eGFR rates of \u3c30 and 30–59 and mL/min/1.73 m2, respectively (). These data provide a snapshot of anemia treatment practices and outcomes in USA ICU patients with varying degrees of renal dysfunction

An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data

The current gold standard for human activity recognition (HAR) is based on the use of cameras. However, the poor scalability of camera systems renders them impractical in pursuit of the goal of wider adoption of HAR in mobile computing contexts. Consequently, researchers instead rely on wearable sensors and in particular inertial sensors. A particularly prevalent wearable is the smart watch which due to its integrated inertial and optical sensing capabilities holds great potential for realising better HAR in a non-obtrusive way. This paper seeks to simplify the wearable approach to HAR through determining if the wrist-mounted optical sensor alone typically found in a smartwatch or similar device can be used as a useful source of data for activity recognition. The approach has the potential to eliminate the need for the inertial sensing element which would in turn reduce the cost of and complexity of smartwatches and fitness trackers. This could potentially commoditise the hardware requirements for HAR while retaining the functionality of both heart rate monitoring and activity capture all from a single optical sensor. Our approach relies on the adoption of machine vision for activity recognition based on suitably scaled plots of the optical signals. We take this approach so as to produce classifications that are easily explainable and interpretable by non-technical users. More specifically, images of photoplethysmography signal time series are used to retrain the penultimate layer of a convolutional neural network which has initially been trained on the ImageNet database. We then use the 2048 dimensional features from the penultimate layer as input to a support vector machine. Results from the experiment yielded an average classification accuracy of 92.3%. This result outperforms that of an optical and inertial sensor combined (78%) and illustrates the capability of HAR systems using...Comment: 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Scienc

A machine vision approach to human activity recognition using photoplethysmograph sensor data

Human activity recognition (HAR) is an active area of research concerned with the classification of human motion. Cameras are the gold standard used in this area, but they are proven to have scalability and privacy issues. HAR studies have also been conducted with wearable devices consisting of inertial sensors. Perhaps the most common wearable, smart watches, comprising of inertial and optical sensors, allow for scalable, non-obtrusive studies. We are seeking to simplify this wearable approach further by determining if wrist-mounted optical sensing, usually used for heart rate determination, can also provide useful data for relevant activity recognition. If successful, this could eliminate the need for the inertial sensor, and so simplify the technological requirements in wearable HAR. We adopt a machine vision approach for activity recognition based on plots of the optical signals so as to produce classifications that are easily explainable and interpretable by non-technical users. Specifically, time-series images of photoplethysmography signals are used to retrain the penultimate layer of a pretrained convolutional neural network leveraging the concept of transfer learning. Our results demonstrate an average accuracy of 75.8%. This illustrates the feasibility of implementing an optical sensor-only solution for a coarse activity and heart rate monitoring system. Implementing an optical sensor only in the design of these wearables leads to a trade off in classification performance, but in turn, grants the potential to simplify the overall design of activity monitoring and classification systems in the future