Three-dimensional modeling of a primary health care clinic in Ho, Ghana: its contribution to student engagement, fundraising, and program planning

Improvements in computer-based technologies can be leveraged to enhance engagement of remote stakeholders with the health needs of a geographically distant community. Three-dimensional (3D) modeling offers a platform to create detailed spatial representations through which stakeholders can experience improvements in shared understanding as well as increased involvement in community health projects occurring anywhere in the world. This case study describes the development of a 3D model of a community health clinic in rural Ghana used to encourage fundraising and sustain global engagement among students at Northwestern University. The resulting ‘virtual clinic’ was achieved quickly and at little cost, suggesting a broader utility of 3D modeling for global health practitioners for increasing donor engagement and resource mapping

Jill Watson: A Virtual Teaching Assistant for Online Education

MOOCs are rapidly proliferating. However, for many MOOCs, the effectiveness of learning is questionable and student retention is low. One recommendation for improving the learning and the retention is to enhance the interaction between the teacher and the students. However, the number of teachers required to provide learning assistance to all students enrolled in all MOOCs is prohibitively high. One strategy for improving interactivity in MOOCs is to use virtual teaching assistants to augment and amplify interaction with human teachers. We describe the use of a virtual teaching assistant called Jill Watson (JW) for the Georgia Tech OMSCS 7637 class on Knowledge-Based Artificial Intelligence. JW has been operating on the online discussion forums of different offerings of the KBAI class since Spring 2016. By now some 750 students have interacted with different versions of JW. In the latest, Spring 2017 offering of the KBAI class, JW autonomously responded to student introductions, posted weekly announcements, and answered routine, frequently asked questions. In this article, we describe the motivations, background, and evolution of the virtual question-answering teaching assistant

Lung Cancer Survival Prediction using Ensemble Data Mining on Seer Data

We analyze the lung cancer data available from the SEER program with the aim of developing accurate survival prediction models for lung cancer. Carefully designed preprocessing steps resulted in removal/modification/splitting of several attributes, and 2 of the 11 derived attributes were found to have significant predictive power. Several supervised classification methods were used on the preprocessed data along with various data mining optimizations and validations. In our experiments, ensemble voting of five decision tree based classifiers and meta-classifiers was found to result in the best prediction performance in terms of accuracy and area under the ROC curve. We have developed an on-line lung cancer outcome calculator for estimating the risk of mortality after 6 months, 9 months, 1 year, 2 year and 5 years of diagnosis, for which a smaller non-redundant subset of 13 attributes was carefully selected using attribute selection techniques, while trying to retain the predictive power of the original set of attributes. Further, ensemble voting models were also created for predicting conditional survival outcome for lung cancer (estimating risk of mortality after 5 years of diagnosis, given that the patient has already survived for a period of time), and included in the calculator. The on-line lung cancer outcome calculator developed as a result of this study is available at http://info.eecs.northwestern.edu:8080/LungCancerOutcomeCalculator/

Poster: A lung cancer mortality risk calculator based on seer data

Abstract — We analyze the lung cancer data available from the SEER program for developing survival prediction models using data mining techniques. The prototype mortality risk calculator developed as a result of this study is available at info.eecs.northwestern.edu:8080/CancerMortalityRiskCalculator Keywords- data mining; lung cancer; mortality; risk calculator. I