34,507 research outputs found
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
The benefits of targeted memory reactivation for consolidation in sleep are contingent on memory accuracy and direct cue-memory associations
Objectives: To investigate how the effects of targeted memory reactivation (TMR) are influenced by memory accuracy prior to sleep and the presence or absence of direct cue-memory associations. Methods: 30 participants associated each of 50 pictures with an unrelated word and then with a screen location in two separate tasks. During picture-location training, each picture was also presented with a semantically related sound. The sounds were therefore directly associated with the picture locations but indirectly associated with the words. During a subsequent nap, half of the sounds were replayed in slow wave sleep (SWS) (TMR). The effect of TMR on memory for the picture locations (direct cue-memory associations) and picture-word pairs (indirect cue-memory associations) was then examined. Results: TMR reduced overall memory decay for recall of picture locations. Further analyses revealed a benefit of TMR for picture locations recalled with a low degree of accuracy prior to sleep, but not those recalled with a high degree of accuracy. The benefit of TMR for low accuracy memories was predicted by time spent in SWS. There was no benefit of TMR for memory of the picture-word pairs, irrespective of memory accuracy prior to sleep. Conclusions: TMR provides the greatest benefit to memories recalled with a low degree of accuracy prior to sleep. The memory benefits of TMR may also be contingent on direct cue-memory associations
Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators.
Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://arrowsmith.psych.uic.edu in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community
Exploratory Analysis of Human Sleep Data
In this thesis we develop data mining techniques to analyze sleep irregularities in humans. We investigate the effects of several demographic, behavioral and emotional factors on sleep progression and on patient\u27s susceptibility to sleep-related and other disorders. Mining is performed over subjective and objective data collected from patients visiting the UMass Medical Center and the Day Kimball Hospital for treatment. Subjective data are obtained from patient responses to questions posed in a sleep questionnaire. Objective data comprise observations and clinical measurements recorded by sleep technicians using a suite of instruments together called polysomnogram. We create suitable filters to capture significant events within sleep epochs. We propose and employ a Window-based Association Rule Mining Algorithm to discover associations among sleep progression, pathology, demographics and other factors. This algorithm is a modified and extended version of the Set-and-Sequences Association Rule Mining Algorithm developed at WPI to support the mining of association rules from complex data types. We analyze both the medical as well as the statistical significance of the associations discovered by our algorithm. We also develop predictive classification models using logistic regression and compare the results with those obtained through association rule mining
Addendum to Informatics for Health 2017: Advancing both science and practice
This article presents presentation and poster abstracts that were mistakenly omitted from the original publication
Association Rules Mining Based Clinical Observations
Healthcare institutes enrich the repository of patients' disease related
information in an increasing manner which could have been more useful by
carrying out relational analysis. Data mining algorithms are proven to be quite
useful in exploring useful correlations from larger data repositories. In this
paper we have implemented Association Rules mining based a novel idea for
finding co-occurrences of diseases carried by a patient using the healthcare
repository. We have developed a system-prototype for Clinical State Correlation
Prediction (CSCP) which extracts data from patients' healthcare database,
transforms the OLTP data into a Data Warehouse by generating association rules.
The CSCP system helps reveal relations among the diseases. The CSCP system
predicts the correlation(s) among primary disease (the disease for which the
patient visits the doctor) and secondary disease/s (which is/are other
associated disease/s carried by the same patient having the primary disease).Comment: 5 pages, MEDINFO 2010, C. Safran et al. (Eds.), IOS Pres
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