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- Genre: Masters Thesis
Description
The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records (EMR) are the digital repository for health data of patients. Nation wide efforts have been made by the federal government to promote the usage of EHRs as they have been found to improve quality of health service. Although EHR systems have been implemented almost everywhere, active use of EHR applications have not replaced paper documentation. Rather, they are often used to store transcribed data from paper documentation after each clinical procedure. This process is found to be prone to errors such as data omission, incomplete data documentation and is also time consuming. This research aims to help improve adoption of real-time EHRs usage while documenting data by improving the usability of an iPad based EHR application that is used during resuscitation process in the intensive care unit. Using Cognitive theories and HCI frameworks, this research identified areas of improvement and customizations in the application that were required to exclusively match the work flow of the resuscitation team at the Mayo Clinic. In addition to this, a Handwriting Recognition Engine (HRE) was integrated into the application to support a stylus based information input into EHR, which resembles our target users’ traditional pen and paper based documentation process. The EHR application was updated and then evaluated with end users at the Mayo clinic. The users found the application to be efficient, usable and they showed preference in using this application over the paper-based documentation.
ContributorsSubbiah, Naveen Kumar (Author) / Patel, Vimla L. (Thesis advisor) / Hsiao, Sharon (Thesis advisor) / Sen, Ayan (Committee member) / Atkinson, Robert K (Committee member) / Arizona State University (Publisher)
Created2018
Description
The purpose of this research was to introduce unsaturated soil mechanics to the undergraduate geotechnical engineering course in a concise and easy to understand manner. Also, it was essential to develop unsaturated soil mechanics teaching material that merges smoothly into current undergraduate curriculum and with sufficient flexibility for broad adaptation by faculty. The learning material consists of three lecture modules and a laboratory module. The lecture modules introduced soil mechanics for the general 3-phase medium condition with the saturated soil as a special case. The three lecture modules that were developed are (1) the stress state variables for unsaturated soils, (2) soil-water characteristic curves, and (3) axis translation. A PowerPoint presentation was created to present each module in an easy to understand manner so that the students will enjoy the learning material. Along with the lecture modules, a laboratory module was developed that reinforced the key aspects and concepts for unsaturated soil behavior. A laboratory manual was created for the Tempe Pressure Cell and Fredlund SWC-150 device (one-dimensional oedometer pressure plate device) in order to give the instructor and institution a choice of which testing equipment best fits their program. Along with the laboratory manuals, an analysis guide was created to help students with constructing SWCCs from their laboratory. A soil type recommendation was also researched for use in the laboratory module. The soil ensured acceptably short equilibrium times along with a wide range or suction values controllable by both testing equipment (Tempe Pressure Cell and Fredlund SWC-150). A silt type soil material was recommended for the laboratory module. As a part of this research, a smooth transition from unsaturated to saturated condition was demonstrated through laboratory volume change experiments using a silt soil tested in an oedometer-type pressure plate device. Three different experiments were conducted: (1) volume change for unsaturated soils in response to suction and net normal stress change, (2) volume change for saturated soils in response to effective stress change, as determined using unsaturated soils testing equipment, and (3) traditional consolidation tests on saturated soil using a conventional consolidometer device.
ContributorsRamirez, Eddy F (Author) / Houston, Sandra (Thesis advisor) / Zapata, Claudia (Thesis advisor) / Savenye, Wilhelmina (Committee member) / Arizona State University (Publisher)
Created2013
Description
Currently, recommender systems are used extensively to find the right audience with the "right" content over various platforms. Recommendations generated by these systems aim to offer relevant items to users. Different approaches have been suggested to solve this problem mainly by using the rating history of the user or by identifying the preferences of similar users. Most of the existing recommendation systems are formulated in an identical fashion, where a model is trained to capture the underlying preferences of users over different kinds of items. Once it is deployed, the model suggests personalized recommendations precisely, and it is assumed that the preferences of users are perfectly reflected by the historical data. However, such user data might be limited in practice, and the characteristics of users may constantly evolve during their intensive interaction between recommendation systems.
Moreover, most of these recommender systems suffer from the cold-start problems where insufficient data for new users or products results in reduced overall recommendation output. In the current study, we have built a recommender system to recommend movies to users. Biclustering algorithm is used to cluster the users and movies simultaneously at the beginning to generate explainable recommendations, and these biclusters are used to form a gridworld where Q-Learning is used to learn the policy to traverse through the grid. The reward function uses the Jaccard Index, which is a measure of common users between two biclusters. Demographic details of new users are used to generate recommendations that solve the cold-start problem too.
Lastly, the implemented algorithm is examined with a real-world dataset against the widely used recommendation algorithm and the performance for the cold-start cases.
Moreover, most of these recommender systems suffer from the cold-start problems where insufficient data for new users or products results in reduced overall recommendation output. In the current study, we have built a recommender system to recommend movies to users. Biclustering algorithm is used to cluster the users and movies simultaneously at the beginning to generate explainable recommendations, and these biclusters are used to form a gridworld where Q-Learning is used to learn the policy to traverse through the grid. The reward function uses the Jaccard Index, which is a measure of common users between two biclusters. Demographic details of new users are used to generate recommendations that solve the cold-start problem too.
Lastly, the implemented algorithm is examined with a real-world dataset against the widely used recommendation algorithm and the performance for the cold-start cases.
ContributorsSargar, Rushikesh Bapu (Author) / Atkinson, Robert K (Thesis advisor) / Chen, Yinong (Thesis advisor) / Chavez-Echeagaray, Maria Elena (Committee member) / Arizona State University (Publisher)
Created2020