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Prescription Information Extraction from Electronic Health Records using BiLSTM-CRF and Word Embeddings

Description

Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records

Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.

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2018-05

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ReL GoalD (Reinforcement Learning for Goal Dependencies)

Description

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft.

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.

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2018-05

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Data Management Behind Machine Learning

Description

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.

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2018-05

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Eat In, Not Out: A Comparative Analysis Between at Home Cooking and Restaurant Dining

Description

This creative project seeks to demonstrate the nutritional and financial benefits of cooking in versus eating out to college age students. We sought to determine what factors significantly differentiated restaurant meals versus home-cooked versions, and how we could share this

This creative project seeks to demonstrate the nutritional and financial benefits of cooking in versus eating out to college age students. We sought to determine what factors significantly differentiated restaurant meals versus home-cooked versions, and how we could share this information with our peers to potentially influence them to make a healthy lifestyle change. The first step was to determine the factors that influence college-aged students eating habits, and was presented with a review of relevant literature in several topics. We researched food literacy in young adults, the impact of fast food, social media's role in healthy eating habits, health behavior change in young adults, and the benefits of home cooking to obtain a general baseline of the knowledge of college-aged students. The initial research was utilized to write more effective blog posts that appropriately addressed our targeted demographic and to determine what platforms would be most appropriate to convey our information. These ideas were taken and then translated into a blog and Instagram account that contained healthy, copycat recipes of popular restaurant meals. We wrote 30 blog posts which were made up of 20 original recipes, 8 nutrition informational posts, and an introduction/conclusion. Finally, a focus group was hosted to ascertain the opinions of our peers, and to determine if they would be willing to make a lifestyle change in the form of cooking more frequently as opposed to eating out regularly. We provided them with a pre and post survey to gather their opinions before and after reviewing the findings of our research and project. We concluded that if given the information in an accessible way, college students are willing to eat in, not out.

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2018-05

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The Effects of Time Restricted Feeding on Mood

Description

Intermittent Fasting (IF) is defined as a cyclical eating pattern where an individual will fast for a specific increment of time, followed by caloric intake periods. Fasting is a crucial part of our ancestors’ adaptation to the stresses of famine

Intermittent Fasting (IF) is defined as a cyclical eating pattern where an individual will fast for a specific increment of time, followed by caloric intake periods. Fasting is a crucial part of our ancestors’ adaptation to the stresses of famine in order to maintain mental acuity and physical abilities during food deprivation. IF influences physiological changes such as: triggers protective metabolic pathways, increases metabolic flexibility and resilience, promotes DNA repair and autophagy, increases microbiome diversity and restores the natural cyclical fluctuations of the gut, increases BDNF expression in mood regulating neuronal circuits, and enhances synaptic plasticity of the brain. Research on the underlying causes of mood disorders has linked impairments in neuroplasticity and cellular resilience to this pathophysiology, which fasting could mitigate. Depression and anxiety are reported as the top impediments to academic performance. Thus, an easily implemented treatment such as intermittent fasting may be an option for combating impaired mental health in college students. This research study tested time restricted feeding (TRF) and its impact on mood states. It was hypothesized that: if college students follow a time restricted feeding pattern, then they will be less moody due to TRF’s effects on the metabolism, brain, and gut. The study consisted of 11 college students: 5 following a four-week adherence to TRF (8am-4pm eating window) and 6 in the control group. The POMS questionnaire was used to measure mood states. The participants height, weight, BMI, body fat %, and POMS scores were tested at the beginning and end of the 4 week intervention. The results were as follows: weight p=0.112 (statistical trend), BMI p=0.058 (nearly significant), body fat % p=0.114 (statistical trend), POMS p=0.014 (statistically significant). The data suggests that following a TRF eating pattern can decrease moodiness and improve mood states.

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2019-05

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Using Machine Learning to Predict the NBA

Description

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and

Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.

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2019-05

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Twitch Streamer-Game Recommender System

Description

Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer

Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer data.

Introduction
As watching video games is becoming more popular, those interested are becoming interested in Twitch.tv, an online platform for guests to watch streamers play video games and interact with them. A streamer is an person who broadcasts them-self playing a video game or some other thing for an audience (the guests of the website.) The site allows the guest to first select the game/category to view and then displays currently active streamers for the guest to select and watch. Twitch records the games that a streamer plays along with the amount of time that a streamer spends streaming that game. This is how the score is generated for a streamer’s game. These three terms form the streamer-game-score (user-item-rating) tuples that we use to train out models.
The our problem’s solution is similar to the purpose of the Netflix prize; however, as opposed to suggesting a user a movie, the goal is to suggest a user a game. We built a model to predict the score that a streamer will have for a game. The score field in our data is fundamentally different from a movie rating in Netflix because the way a user influences a game’s score is by actively streaming it, not by giving it an score based off opinion. The dataset being used it the Twitch.tv dataset provided by Isaac Jones [1]. Also, the only data used in training the models is in the form of the streamer-game-score (user-item-rating) tuples. It will be known if these data points with limited information will be able to give an accurate prediction of a streamer’s score for a game. SVD and SVD++ are the baseis of the models being trained and tested. Scikit’s Surprise library in Python3 is used for the implementation of the models.

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2019-05

Detecting Propaganda Bots on Twitter Using Machine Learning

Description

Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on

Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect these bots. The paper focuses on my development and process of training classifiers and using them to create a user-facing server that performs prediction functions automatically. The learning goals of this project were detailed, the focus of which was to learn some form of machine learning architecture. I needed to learn some aspect of large data handling, as well as being able to maintain these datasets for training use. I also needed to develop a server that would execute these functionalities on command. I wanted to be able to design a full-stack system that allowed me to create every aspect of a user-facing server that can execute predictions using the classifiers that I design.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.

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2019-05

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Content Analysis of Existing Nutrition Marketing Materials in Central Arizona Schools

Description

The objective of this study was to evaluate and compare the content of nutrition marketing materials within the cafeterias of schools in Central Arizona. By collecting photographs of marketing material from three elementary schools, one K-8 school, three middle schools

The objective of this study was to evaluate and compare the content of nutrition marketing materials within the cafeterias of schools in Central Arizona. By collecting photographs of marketing material from three elementary schools, one K-8 school, three middle schools and three high schools, 59 pieces of nutrition marketing were gathered. The schools chosen were a convenience sample and selected from schools that were already participating in ASU' s School Lunch Study. The photographs were sorted by grade level and then coded quantitatively and qualitatively for their purpose, visual components, strategies used and relevance. Results from this novel study provided insight into prevalence, size, textual content, educational content, strategies for fruit and vegetable marketing, messaging and overall design of existing nutrition marketing within the sample schools. This study found that the prevalence of nutrition marketing within all school cafeterias appeared to be low, particularly within elementary and middle schools. Diverse types of messaging were present among elementary, middle and high schools and a variety of appeals were utilized with little consistency. Many of the strategies used in the nutrition marketing appeared disconnected from the population it was intended to appeal to. Educational components were notably lacking within middle school cafeterias but were often effectively integrated into high school nutrition marketing. The results are unique to this population, and further research is required to evaluate the content of existing nutrition material on a larger scale, so efforts can be made to improve the persuasiveness of nutrition marketing in promoting fruit and vegetable consumption.

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2018-12

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Examination of an Organometallic Complex on Insulin Resistance in Periadolescent Male Rats Following a 10-week High Fat Diet

Description

With the rising prevalence of obesity and diabetes, novel treatments to help mitigate or prevent symptoms of these conditions are warranted. Prior studies have shown that fossilized plant materials found in soil lowers blood sugar in a mouse model of

With the rising prevalence of obesity and diabetes, novel treatments to help mitigate or prevent symptoms of these conditions are warranted. Prior studies have shown that fossilized plant materials found in soil lowers blood sugar in a mouse model of diabetes. The goal of this study is to determine whether a similar organometallic complex (OMC) could prevent insulin resistance in the skeletal muscle brought on by chronic high fat intake by examining the protein expression of key enzymes in the insulin signaling pathway and examining glucoregulatory measures. Six-week-old periadolescent male Sprague-Dawley rats (n=42) were randomly chosen to be fed either a high fat diet (HFD) (20% protein, 20% carbohydrates [6.8% sucrose], 60% fat) or a standard chow diet (18.9% protein, 57.33% carbohydrates, 5% fat) for 10 weeks. Rats from each diet group were then randomly assigned to one of three doses of OMC (0, 0.6, 3.0 mg/mL), which was added to their drinking water and fasting blood glucose was measured at baseline and again at 10 weeks. After 10 weeks, rats were euthanized, and soleus muscle samples were isolated, snap-frozen, and stored at -80°C until analyses. Fasting plasma glucose was measured using a commercially available glucose oxidase kit. Following 6 and 10 weeks, HFD rats developed significant hyperglycemia (p<0.001 and p=0.025) compared to chow controls which was prevented by high dose OMC (p=0.021). After 10 weeks, there were significant differences in fasting serum insulin between diets (p=0.009) where levels were higher in HFD rats. No significant difference was seen in p-PI3K expression between groups. These results suggest that OMC could prevent insulin resistance by reducing hyperglycemia. Further studies are needed to characterize the effects of diet and OMC on the insulin signaling pathway in skeletal muscle, the main site of postprandial glucose disposal. This study was supported by a grant from Isagenix International LLC as well as funds from Barrett, the Honors College at Arizona State University, Tempe Campus.

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2018-12