Predicting re-finding activity and difficulty

In this study, we address the problem of identifying if users are attempting to re-find information and estimating the level of difficulty of the re- finding task. We propose to consider the task information (e.g. multiple queries and click information) rather than only queries. Our resultant prediction models are shown to be significantly more accurate (by 2%) than the current state of the art. While past research assumes that previous search history of the user is available to the prediction model, we examine if re-finding detection is possible without access to this information. Our evaluation indicates that such detection is possible, but more challenging. We further describe the first predictive model in detecting re-finding difficulty, showing it to be significantly better than existing approaches for detecting general search difficulty

Psychological, emotional and social impairments are associated with adherence and healthcare spending in type 2 diabetic patients: an observational study

OBJECTIVE: The aim of the present study was to assess the association among anxiety, depression, stress, social support and emotional abilities with adherence and healthcare spending in type 2 diabetic patients. PATIENTS AND METHODS: Sixty-four patients were enrolled and completed: Interpersonal Processes of Care (IPC), 20-item Toronto Alexithymia Scale (TAS-20), Rapid Stress Assessment Scale (RSAS), Morisky Medication Adherence Scale (MMAS-4), International Physical Activity Questionnaire (IPAQ)-Short Form and a socio-anamnestic questionnaire regarding also the healthcare spending. RESULTS: Mathematical linear regressions models were performed showing the predictive effects of: anxiety and social support scores (RSAS) on adherence levels (respectively p =. 019; p =. 016); adherence levels on anxiolytic use (p =.04); aggressiveness scores (RSAS) on the number of general check-ups (p =.031); TAS-20 and physician-patient communication (IPC) on the number of hospitalization days (respectively p=.001; p=.008); physician patient decision making (IPC) scores on physical activity (IPAQ) levels (p=.025); physical activity (IPAQ) on the number of medical examinations (p=.039). CONCLUSIONS: An association among psychosocial impairment, adherence and health- care spending was found. Future studies should investigate the effect of a brief psychological intervention in increasing adherence levels and reducing the healthcare spending in this clinical population

Am I Done? Predicting Action Progress in Videos

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of interaction applications. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. To provide a general definition of action progress, we ground our work in the linguistics literature, borrowing terms and concepts to understand which actions can be the subject of progress estimation. As a result, we define a categorization of actions and their phases. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets

Goal-setting And Achievement In Activity Tracking Apps: A Case Study Of MyFitnessPal

Activity tracking apps often make use of goals as one of their core motivational tools. There are two critical components to this tool: setting a goal, and subsequently achieving that goal. Despite its crucial role in how a number of prominent self-tracking apps function, there has been relatively little investigation of the goal-setting and achievement aspects of self-tracking apps. Here we explore this issue, investigating a particular goal setting and achievement process that is extensive, recorded, and crucial for both the app and its users' success: weight loss goals in MyFitnessPal. We present a large-scale study of 1.4 million users and weight loss goals, allowing for an unprecedented detailed view of how people set and achieve their goals. We find that, even for difficult long-term goals, behavior within the first 7 days predicts those who ultimately achieve their goals, that is, those who lose at least as much weight as they set out to, and those who do not. For instance, high amounts of early weight loss, which some researchers have classified as unsustainable, leads to higher goal achievement rates. We also show that early food intake, self-monitoring motivation, and attitude towards the goal are important factors. We then show that we can use our findings to predict goal achievement with an accuracy of 79% ROC AUC just 7 days after a goal is set. Finally, we discuss how our findings could inform steps to improve goal achievement in self-tracking apps

Nowcasting with Google Trends : a keyword selection method

Search engines, such as Google, keep a log of searches entered into their websites. Google makes this data publicly available with Google Trends in the form of aggregate weekly search term volume. Aggregate search volume has been shown to be able to nowcast (i.e. compute real-time assessment of current activity) a variety of variables such as influenza outbreaks, financial market fluctuations, unemployment and retail sales. Although identifying appropriate keywords in Google Trends is an essential element of using search data, the recurring difficulty identified in the literature is the lack of a technique to do so. Given this, the main goal of this paper is to put forward a method (the "backward induction method") of identifying and extracting keywords from Google Trends relevant to economic variables

Choosing the lesser of two evils, the better of two goods: Specifying the roles of ventromedial prefrontal cortex and dorsal anterior cingulate in object choice

The ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate cortices (ACd) are considered important for reward-based decision making. However, work distinguishing their individual functional contributions has only begun. One aspect of decision making that has received little attention is that making the right choice often translates to making the better choice. Thus, response choice often occurs in situations where both options are desirable (e.g., choosing between mousse au chocolat or crème caramel cheesecake from a menu) or, alternatively, in situations where both options are undesirable. Moreover, response choice is easier when the reinforcements associated with the objects are far apart, rather than close together, in value. We used functional magnetic resonance imaging to delineate the functional roles of the vmPFC and ACd by investigating these two aspects of decision making: (1) decision form (i.e., choosing between two objects to gain the greater reward or the lesser punishment), and (2) between-object reinforcement distance (i.e., the difference in reinforcements associated with the two objects). Blood oxygen level-dependent (BOLD) responses within the ACd and vmPFC were both related to decision form but differentially. Whereas ACd showed greater responses when deciding between objects to gain the lesser punishment, vmPFC showed greater responses when deciding between objects to gain the greater reward. Moreover, vmPFC was sensitive to reinforcement expectations associated with both the chosen and the forgone choice. In contrast, BOLD responses within ACd, but not vmPFC, related to between-object reinforcement distance, increasing as the distance between the reinforcements of the two objects decreased. These data are interpreted with reference to models of ACd and vmPFC functioning

Predicting sexual problems in women: The relevance of sexual excitation and sexual inhibition

This is the post-print version of the article. The official published version can be obtained from the link below.Data from a non-clinical sample of 540 heterosexual women were used to examine the relationships between scores on the Sexual Excitation/Sexual Inhibition Inventory for Women (SESII-W) and ratings of current sexual problems, lifetime arousal difficulty, lifetime orgasm difficulty, and lifetime problems with low sexual interest. Multiple regression analyses also included several demographic/background variables as predictors: age, full-time employment, completed college, children in household, married, health ratings, importance of sex, and whether the woman was in a sexual relationship. The strongest statistical predictors of both current and lifetime sexual problems were the SESII-W inhibition factors Arousal Contingency and Concerns about Sexual Function. Demographic factors did not feature largely in any of the models predicting sexual problems even when statistically significant relationships were found. If future research supports the predictive utility of the SESII-W in identifying women who are more likely to experience sexual difficulties, these scales may be used as prognostic factors in treatment studies.This study was funded, in part, by a grant from the Lilly Centre for Women's Health

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa