Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Development and validation of two influenza assessments : exploring the impact of knowledge and social environment on health behaviors

Title from PDF of title page (University of Missouri--Columbia, viewed on May 30, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Lloyd H. BarrowVita.Ph. D. University of Missouri--Columbia 2011."December 2011"Assessments of knowledge and perceptions about influenza were developed for high school students, and used to determine how knowledge, perceptions, and demographic variables relate to students taking precautions and their odds of getting sick. Assessments were piloted and validated using the Rasch model (n = 205). The 2-parameter logistic model and the k-means clustering algorithm were used for scoring of final participants (n = 410). Kendall-tau correlations were evaluated at the [alpha]= 0.05 level, multinomial logistic regression was used to identify the best predictors and to test for interactions, and neural networks were used to test how well precautions and illness can be predicted using the significant correlates. Knowledge was positively correlated to compliance with vaccination, hand washing frequency, and respiratory etiquette, and negatively correlated with hand sanitizer use. Perceived risk was positively correlated to compliance with flu vaccination; perceived complications to personal distancing and staying home when sick. Perceived risk and complications increased with reported illness severity. Perceived barriers decreased compliance with vaccination, hand washing, and respiratory etiquette. Factors such as gender, ethnicity, and school, had effects on more than one precaution. Hand washing quality and frequency could be predicted moderately well. Implications for future uses of the instruments and development of interventions regarding influenza in high schools are discussed.Includes bibliographical reference

Flow and heat transfer properties of Mono Craters rhyolites : effects of temperature, water content, and crystallinity

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Title from PDF of title page (University of Missouri--Columbia, viewed on October 5, 2009).Thesis advisor: Dr. Alan G. Whittington.M.S. University of Missouri--Columbia 2008.The nature of volcanic processes, including rate of magma ascent, exsolution of volatiles, eruption style, and flow distance, is highly dependent on the viscosity of the associated magma and its ability to transfer heat. We present measurements of the viscosity and thermal diffusivity of Quaternary rhyolitic lava flows from Mono Craters, California. We quantify the effects of temperature, dissolved water content, and crystallinity on viscosity and thermal diffusivity. We use the parallel plate and concentric cylinder methods to obtain viscosity measurements between 5 x 103̂ to 8 x 101̂2 Pas, from superliquidus conditions to the glass transition; the laser flash (LFA) method to measure thermal diffusivity of samples between room and subliquidus temperatures. The investigated obsidian samples, collected from three different flow lobes, contain between 0.1 and 1.1 wt.% H2O, and less than 2 vol.% crystals. We also remelted one sample from each lobe in a muffle furnace to produce nearly anhydrous, crystal free glass. We fit our viscosity data to four literature models relevant to rhyolitic melts, two developed specifically for rhyolites and two global models. We add to this by presenting our own models based on the empirical TVF equation and the theory-based Adam-Gibbs equation, finding that the Adam- Gibbs model fits our data slightly better. We also present a model relating the thermal diffusivity of the samples to their crystal contents and temperatures below the glass transition. Water has a negligible effect on thermal diffusivity at the low concentrations in the samples studied.Includes bibliographical references

Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach

As pre-diagnostic technologies are becoming increasingly accessible, using them to improve the quality of care available to dementia patients and their caregivers is of increasing interest. Specifically, we aim to develop a tool for non-invasively assessing task performance in a simple gaming application. To address this, we have developed Caregiver Assessment using Smart Gaming Technology (CAST), a mobile application that personalizes a traditional word scramble game. Its core functionality uses a Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide customized performance measures for each user of the system. With CAST, we match the relative level of difficulty of play using the individual's ability to solve the word scramble tasks. We provide an analysis of the preliminary results for determining task difficulty, with respect to our current participant cohort.Comment: 7 pages, 1 figures, 6 table

Exploring the Impact of Knowledge and Social Environment on Influenza Prevention and Transmission in Midwestern United States High School Students

We used data from a convenience sample of 410 Midwestern United States students from six secondary schools to develop parsimonious models for explaining and predicting precautions and illness related to influenza. Scores for knowledge and perceptions were obtained using two-parameter Item Response Theory (IRT) models. Relationships between outcome variables and predictors were verified using Pearson and Spearman correlations, and nested [student within school] fixed effects multinomial logistic regression models were specified from these using Akaike’s Information Criterion (AIC). Neural network models were then formulated as classifiers using 10-fold cross validation to predict precautions and illness. Perceived barriers against taking precautions lowered compliance with the CDC recommended preventative practices of vaccination, hand washing quality, and respiratory etiquette. Perceived complications from influenza illness improved social distancing. Knowledge of the influenza illness was a significant predictor for hand washing frequency and respiratory etiquette. Ethnicity and gender had varying effects on precautions and illness severity, as did school-level effects: enrollment size, proficiency on the state’s biology end-of-course examination, and use of free or reduced lunch. Neural networks were able to predict illness, hand hygiene, and respiratory etiquette with moderate success. Models presented may prove useful for future development of strategies aimed at mitigation of influenza in high school youths. As more data becomes available, health professionals and educators will have the opportunity to test and refine these models

Measuring Science Teachers\u27 Emotional Experiences with Evolution Using Real World Scenarios

Low acceptance of evolution remains an obstacle to quality biology instruction. We develop and utilize a novel assessment which measures emotional experience in light of real-world evolution education scenarios. We presented 296 science teachers 4 pro-evolution and 8 anti-evolution scenarios and asked them to rate their levels of joy, anger, sadness, fear, disgust, shame, and guilt elicited by that scenario on an ordinal 5-point scale. We used exploratory factor analysis to extract the most important dimensions in the teachers’ responses, Rasch analysis to explore the validity of the extracted subscales, and stepwise regression to find the most important factors driving emotional dispositions. We extracted 3 factors: (1) pro-evolution experience (positive emotions on pro-evolution and negative emotions on anti-evolution scenarios), (2) anti-evolution experience (negative emotions on pro-evolution and positive emotions on anti-evolution scenarios), and (2) feelings of regret over anti-evolution scenarios (shame and guilt on anti-evolution scenarios). Acceptance of evolution facts and a non-theistic religious orientation were positively related to pro-evolution experience. Anti-evolution experience was predicted by lack of microevolution acceptance and lack of teacher preparation. Feelings of regret around anti-evolution scenarios were driven by acceptance of evolution facts and lower levels of teacher preparation. This work advances our understanding of how teachers relate affectively to the theory of evolution and offers empirical insight into ways to improve dispositions about evolution

Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events

Background: Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective: The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of thetweets and topics of discussion over 12 months of data collection. Methods: This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and “Bacillus anthracis”, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion. Results: Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F1-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories. Conclusions: This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats

Toward Mental Effort Measurement Using Electrodermal Activity Features

The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant\u27s self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions