Representation and Analysis of Multi-Modal, Nonuniform Time Series Data: An Application to Survival Prognosis of Oncology Patients in an Outpatient Setting

The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which was explored initially through discrete sampling and behavioral representation. The length of survival was evaluated with both classification and regression methods initially. The first conclusion determined that including more discrete samples in the model showed no statistical benefit and the addition of behavioral approaches did improve the prognostic accuracy. From this result, the adaption of Piecewise Aggregate Approximation was made to accommodate the multi-modal time series data of the outpatient clinical data, and evaluated with the regression methodologies. This representation approach demonstrated promise due to the simplicity but had decreased performance in the length of survival prognosis compared with behavioral representation and discrete samples approach. A solution was a new representation approach made which incorporates a genetic algorithm to select the window boundaries of the Piecewise Aggregate Approximation method. This selection is based on the fraction of the Piecewise Aggregate Approximation windows that contain values other than zero. The new representation improved the performance in some cases by a 20% reduction in median relative error

Perspective of teenagers on traits and research associated with electrical and Computer Engineers and their research

© 2016 IEEE. Gender and diversity balance issues are prominent in the field of engineering. The way engineers and their research are perceived are two areas that contribute to youth deciding on careers in science and engineering. The perceptions of the youth on traits of Electrical and Computer Engineers (ECEs) and their research were explored through a survey of summer youth program students at Michigan Tech. Five different week long engineering programs were offered with surveys presented at the start and end of the contact times to observe how the activities and outreach impact the student perceptions. The perspectives of the youth show a lack of gender related trait association. After all different levels of contact, research area association with ECE was expanded. The largest areas displaying increased association were socially impacted areas, such as medicine. Increasing the association with these areas may help increase the interest of women in becoming engineers using the demographics of females in sciences as a statistical guide. This has shown that the benefit of engineering programs on perceptions and association is indifferent to curriculum and contact duration; that every bit of outreach makes a difference for the youth and prospects for improving gender balance in engineering

Representation of clinical information in outpatient oncology for prognosis using regression

The determination of length of survival, or prognosis, is often viewed through statistical hazard models or with respect to a future reference time point in a classification approach (e.g., survival after 2 or 5 years). In this research, regression was used to determine a patient’s prognosis. Also, multiple behavioral representations of clinical data, including difference trends and splines, are considered for predictor variables, which is different from demographic and tumor characteristics often used. With this approach the amount of clinical samples considered from the available patient data in the model in conjunction with the behavioral representation was explored. The models with the best prognostic performance had data representations that included limited clinical samples and some behavioral interpretations

Representation and incorporation of clinical information in outpatient oncology prognosis using Bayesian networks and Naïve Bayes

© 2016 IEEE. Many studies have focused on prognosis for oncology patients with the following characteristics: an inpatient setting (uniform sampling), binary outcomes, and predictor variables of patient demographics and tumor characteristics. This paper examines the problem of predicting prognosis in an outpatient setting (non-uniform sampling), discrete outcomes, and predictor variables of clinical observations. In particular, we consider how to represent the clinical observational data and reason over the prognosis using Bayesian networks (BN) and Naíve Bayes (NB). Different representations include trend behaviors using splines and differences over a time period and the clinical observations themselves. The best models were able to outperform the majority classifier with 8.5% (BN) and 10.2% (NB) higher accuracies in predicting a patient\u27s length of survival. The models with highest predictive performance both include a temporal behavioral representation

Working on how to solve the never ending problem of diversity

There are many challenges in changing the diversity in engineering. In the Catalyzing Collaborative Conversation, we explored ways to change the diversity climate through outreach, recruitment, and retention methods to help students succeed and achieve the goal of becoming an engineer