Multi-stream Longitudinal Data Analysis using Deep Learning

Longitudinal healthcare data encompasses all tasks where patients information are collected at multiple follow-up times. Analyzing this data is critical in addressing many real world problems in healthcare such as disease prediction and prevention. In this thesis, technical challenges in analyzing longitudinal administrative claims data are addressed and novel deep learning based models are proposed for multi-stream data analysis and disease prediction tasks. These algorithms and frameworks are assessed mainly on substance use disorders prediction tasks and specifically designed to tackled these disorders. Substance use disorder is a public health crisis costing the US an estimated $740 billion annually in healthcare, lost workplace productivity, and crime. Early identification and engagement of individuals at risk of developing a substance use disorder is a critical unmet need in healthcare which can be achieved by producing automatic artificial intelligence based tools trained using big healthcare data. In fact, healthcare data can be harnessed together with artificial intelligence and machine learning to advance our understanding of factors that increase the propensity for developing different diseases as well as those that aid in the treatment of these disorders. Here in, a disease prediction framework is first proposed based on recurrent neural networks. This framework includes three components: 1) data pre-processing, 2) disease prediction using long short term memory models, and 3) hypothesis exploration by varying the models and the inputs. This framework is assessed using two use cases: substance use disorder prediction and mild cognitive impairment prediction. Experimental results show that this proposed model can efficiently analyze patients\u27 data and creates efficient disease prediction tools. Second, the limitationsof current deep learning models including long short term memory models in claimsdata analysis are detected and addressed, and a novel model based on the transformer models is proposed. In fact, leveraging the real-world longitudinal claims data, a novel multi-stream transformer model is proposed for predicting opioid use disorder as an important case of substance use disorders. This model is designed to simultaneously analyze multiple types of data streams, such as medications, diagnoses, procedures and demographics, by attending to segments within and across these data streams. The proposed model tested on the IBM MarketScan data showed significantly better performance than the traditional models and recently developed deep learning models

Contract and Grant Awards Fiscal Year 2001

I invite you to read this report Contract and Grant Awards FY 2001, which lists contract and grant awards received by the University of New Mexico (UNM) during the period from July 1, 2000 - June 30, 2001. These awards represent new funds that were acquired during FY01 by the main campus, branch campuses and education centers, and the Health Sciences Center (HSC). The HSC includes the School of Medicine, College of Nursing and College of Pharmacy. The awards received for FY01 total $235.7M, of which$147.2M is attributed to the main campus and $88.5M to HSC. These awards assist in providing resources that are necessary to support and enhance the quality of research and teaching at UNM, as well as the opportunities for students to be trained in state-of-the-art laboratories in a variety of disciplines. Please join me in thanking our dedicated faculty, staff and students involved in the sponsored research, public service and instruction efforts at UNM. It is their successful endeavors that enhance the University\u27s visibility at the national and international levels, as well as contribute to the economic growth of New Mexico and the region. Thanks are also due to a number of individuals who have helped in preparing this report. In particular, I would like to acknowledge the efforts of Denise Wallen, Ann Powell and Valerie Roybal of the Office of the Vice Provost for Research, and Marcia Sletten and Lee Gulbransen of the Health Sciences Center. I welcome your comments and questions with respect to this report, and other issues related to research activities at the University of New Mexico. Terry L. Yates Vice Provost for Researc

UMSL Bulletin 2019-2020

The University Bulletin/Course Catalog 2019-2020 Edition.https://irl.umsl.edu/bulletin/1083/thumbnail.jp

A theoretical and practical approach to a persuasive agent model for change behaviour in oral care and hygiene

There is an increased use of the persuasive agent in behaviour change interventions due to the agent‘s features of sociable, reactive, autonomy, and proactive. However, many interventions have been unsuccessful, particularly in the domain of oral care. The psychological reactance has been identified as one of the major reasons for these unsuccessful behaviour change interventions. This study proposes a formal persuasive agent model that leads to psychological reactance reduction in order to achieve an improved behaviour change intervention in oral care and hygiene. Agent-based simulation methodology is adopted for the development of the proposed model. Evaluation of the model was conducted in two phases that include verification and validation. The verification process involves simulation trace and stability analysis. On the other hand, the validation was carried out using user-centred approach by developing an agent-based application based on belief-desire-intention architecture. This study contributes an agent model which is made up of interrelated cognitive and behavioural factors. Furthermore, the simulation traces provide some insights on the interactions among the identified factors in order to comprehend their roles in behaviour change intervention. The simulation result showed that as time increases, the psychological reactance decreases towards zero. Similarly, the model validation result showed that the percentage of respondents‘ who experienced psychological reactance towards behaviour change in oral care and hygiene was reduced from 100 percent to 3 percent. The contribution made in this thesis would enable agent application and behaviour change intervention designers to make scientific reasoning and predictions. Likewise, it provides a guideline for software designers on the development of agent-based applications that may not have psychological reactance

2017-2018 Boise State University Graduate Catalog

The graduate catalog describes the graduate programs offered by Boise State University and the policies, procedures, and requirements that govern those programs. Other pertinent university publications are the Boise State University Student Handbook, and the Boise State University Policy Manual. All of these publications are available online at www.boisestate.edu along with the online schedule of classes. Prospective students are also encouraged to contact the graduate program coordinator of the program of interest for additional information