Developing a Clinical Decision Support System for Prediction Postoperative Coronary Artery Bypass Grafting Infection in Diabetic Patients

Background: Postoperative infection in Coronary Artery Bypass Graft (CABG) is one of the most common complications for diabetic patients, due to an increase in the hospitalization and cost. To address these issues, it is necessary to apply some solutions. Objective: The study aimed to the development of a Clinical Decision Support System (CDSS) for predicting the CABG postoperative infection in diabetic patients.Material and Methods: This developmental study is conducted on a private hospital in Tehran in 2016. From 1061 CABG surgery medical records, we selected 210 cases randomly. After data gathering, we used statistical tests for selecting related features. Then an Artificial Neural Network (ANN), which was a one-layer perceptron network model and a supervised training algorithm with gradient descent, was constructed using MATLAB software. The software was then developed and tested using the receiver operating characteristic (ROC) diagram and the confusion matrix. Results: Based on the correlation analysis, from 28 variables in the data, 20 variables had a significant relationship with infection after CABG (P<0.05). The results of the confusion matrix showed that the sensitivity of the system was 69%, and the specificity and the accuracy were 97% and 84%, respectively. The Receiver Operating Characteristic (ROC) diagram shows the appropriate performance of the CDSS.  Conclusion: The use of CDSS can play an important role in predicting infection after CABG in patients with diabetes. The designed software can be used as a supporting tool for physicians to predict infections caused by CABG in diabetic patients as a susceptible group. However, other factors affecting infection must also be considered for accurate prediction

TIP: Tangible e-Ink Paper Manipulatives for Classroom Orchestration

While digital tools are more and more used in classrooms, teachers' common practice remains to use photocopied paper documents to share and collect learning exercises from their students. With the Tangible e-Ink Paper (TIP) system, we aim to explore the use of tangible manipulatives interacting with paper sheets as a bridge between digital and paper traces of learning. Featuring an e-Ink display, a paper-based localisation system and a wireless connection, TIPs are envisioned to be used as a versatile tool across various curriculum activities. In this paper, we present the design principles of TIPs and a first functional prototype. We conclude by presenting future works in the evaluation of TIPs as a distributed sensor for teachers in their classroom, including learning scenario examples to illustrate our statements

Orchestration of Robotic Activities in Classrooms: Challenges and Opportunities

Bringing robots into classrooms presents a new set of challenges for classroom management and teacher support compared to traditional technology-enhanced learning and has been left almost unexplored by the research community. In this paper, we present the opportunities and challenges of orchestrating Educational Robotics (ER) activities in classrooms. To support our discussion, we present a case study of 25 students working in pairs using handheld robots to engage in a computational thinking activity. While performing the activity, students’ behavioural information was sent from the robots to an orchestration dashboard that was used in a debriefing activity. Although this work is in its preliminary stages, it contributes to framing the challenges that need to be addressed to realistically scale-up usage of ER in classrooms