Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services

A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial data

Modeling Epidemics Spreading on Social Contact Networks

Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion

Cluster-based Epidemic Control Through Smartphone-based Body Area Networks

Increasing population density, closer social contact, and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be isolated to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphonebased body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks

Interference Mitigation for Cyber-Physical Wireless Body Area Network System Using Social Networks

Wireless body area networks (WBANs) are cyber-physical systems that emerged as a key technology to provide real-time health monitoring and ubiquitous healthcare services. WBANs could operate in dense environments such as in a hospital and lead to a high mutual communication interference in many application scenarios. The excessive interferences will significantly degrade the network performance, including depleting the energy of WBAN nodes more quickly and even eventually jeopardize people\u27s lives because of unreliable (caused by the interference) healthcare data collections. Therefore, it is critical to mitigate the interference among WBANs to increase the reliability of the WBAN system while minimizing the system power consumption. Many existing approaches can deal with communication interference mitigation in general wireless networks but are not suitable for WBANs because of ignoring the social nature of WBANs by them. Unlike the previous research, we for the first time propose a power game based approach to mitigate the communication interferences for WBANs based on the people\u27s social interaction information. Our major contributions include: 1) modeling the inter-WBANs interference and determine the distance distribution of the interference through both theoretical analysis and Monte Carlo simulations; 2) developing social interaction detection and prediction algorithms for people carrying WBANs; and 3) developing a power control game based on the social interaction information to maximize the system\u27s utility while minimize the energy consumption of WBANs system. The extensive simulation results show the effectiveness of the power control game for inter-WBAN interference mitigation using social interaction information. Our research opens a new research vista of WBANs using social networks

A survey of big data research

Big data create values for business and research, but pose significant challenges in terms of networking, storage, management, analytics, and ethics. Multidisciplinary collaborations from engineers, computer scientists, statisticians, and social scientists are needed to tackle, discover, and understand big data. This survey presents an overview of big data initiatives, technologies, and research in industries and academia, and discusses challenges and potential solutions

Using the bootstrap to establish statistical significance for relative validity comparisons among patient-reported outcome measures

BACKGROUND: Relative validity (RV), a ratio of ANOVA F-statistics, is often used to compare the validity of patient-reported outcome (PRO) measures. We used the bootstrap to establish the statistical significance of the RV and to identify key factors affecting its significance. METHODS: Based on responses from 453 chronic kidney disease (CKD) patients to 16 CKD-specific and generic PRO measures, RVs were computed to determine how well each measure discriminated across clinically-defined groups of patients compared to the most discriminating (reference) measure. Statistical significance of RV was quantified by the 95% bootstrap confidence interval. Simulations examined the effects of sample size, denominator F-statistic, correlation between comparator and reference measures, and number of bootstrap replicates. RESULTS: The statistical significance of the RV increased as the magnitude of denominator F-statistic increased or as the correlation between comparator and reference measures increased. A denominator F-statistic of 57 conveyed sufficient power (80%) to detect an RV of 0.6 for two measures correlated at r = 0.7. Larger denominator F-statistics or higher correlations provided greater power. Larger sample size with a fixed denominator F-statistic or more bootstrap replicates (beyond 500) had minimal impact. CONCLUSIONS: The bootstrap is valuable for establishing the statistical significance of RV estimates. A reasonably large denominator F-statistic (F \u3e 57) is required for adequate power when using the RV to compare the validity of measures with small or moderate correlations (r \u3c 0.7). Substantially greater power can be achieved when comparing measures of a very high correlation (r \u3e 0.9)

A Pilot Randomized Controlled Trial of a Videoconferencing Smoking Cessation Intervention for Korean American Women: Preliminary Findings

Introduction: Korean American women prefer online or telephone smoking cessation interventions that can be remotely accessed from home. However, these interventions have been found ineffective for the group. Methods: This study is a pilot clinical trial testing the feasibility and acceptability of a videoconferencing smoking cessation intervention for Korean American women and compared its preliminary efficacy with a telephone-based smoking cessation intervention. Korean women in the United States were recruited nationwide and randomly assigned at a ratio of 1:1 to either a video arm or a telephone arm. Participants in both arms received eight 30-minute weekly individualized counseling sessions of a culturally adapted smoking cessation intervention and nicotine patches for 8 weeks. They were followed up at post-quit 1, 2, and 3 months. Results: A total of 168 Korean Americans were assessed for eligibility, 77 were determined to eligible and 49 participated in the study. The videoconferencing intervention was acceptable and feasible for women under 50 years, whereas it was not for older women. The videoconferencing intervention produced abstinence rates of 67% at post-quit 1 month and 42% at post-quit 3 months based on self-report. The rate at post-quit 3 months dropped to 33% when those women whose abstinence could not be validated with salivary cotinine tests were treated as smoking. Abstinence rates in the telephone arm did not differ from those in the video arm. Conclusion: Findings suggest that videoconferencing smoking cessation intervention may be feasible and acceptable for Korean American women under 50 years. However, for older Korean American women, the intervention may not be feasible and telephone-based intervention seems to be just as effective if smoking cessation intervention components are adapted at a deep structural level of Korean culture by integrating its core cultural values and addressing psychosocial, social and environmental forces affecting the behavior

Challenges in sodium intake reduction and meal consumption patterns among participants with metabolic syndrome in a dietary trial

BACKGROUND: Dietary guidelines suggest limiting daily sodium intake to METHODS: Two hundred forty participants with metabolic syndrome enrolled in a dietary intervention trial to lose weight and improve dietary quality. Three 24-hour dietary recalls were collected at each visit which provided meal patterns and nutrient data, including sodium intake. A secondary data analysis was conducted to examine sodium consumption patterns at baseline and at one-year study visits. Sodium consumption patterns over time were examined using linear mixed models. RESULTS: The percentage of meals reported eaten in the home at both baseline and one-year follow-up was approximately 69%. Follow-up for the one-year dietary intervention revealed that the participants who consumed sodium greater than 2,300 mg/d declined from 75% (at baseline) to 59%, and those that consumed higher than 1,500 mg/d declined from 96% (at baseline) to 85%. Average sodium intake decreased from 2,994 mg at baseline to 2,558 mg at one-year (P \u3c 0.001), and the sodium potassium ratio also decreased from 1.211 to 1.047 (P \u3c 0.001). Sodium intake per meal varied significantly by meal type, location, and weekday, with higher intake at dinner, in restaurants, and on weekends. At-home lunch and dinner sodium intake decreased (P \u3c 0.05), while dinner sodium intake at restaurant/fast food chains increased from baseline to one-year (P \u3c 0.05). CONCLUSION: Sodium intake for the majority of participants exceeded the recommended dietary guidelines. Findings support actions that encourage low-sodium food preparation at home and encourage public health policies that decrease sodium in restaurants and prepared foods

The addition of a pH-sensitive gel improves microemulsion stability for the targeted removal of colonic ammonia

<p>Abstract</p> <p>Background</p> <p>We prepared an oral W/O microemulsion for the removal of colonic ammonia (ME-RCA). The effect of this microemulsion was influenced by the digestion process in the gastrointestinal tract. In this paper, we aim to show that stability was improved by using a microemulsion-based gel for the removal of colonic ammonia (MBG-RCA).</p> <p>Methods</p> <p>MBG-RCA was prepared by adding sodium alginate to the ME-RCA. MBG-RCA and ME-RCA were passed through a simulated gastrointestinal environment, and the amount of colonic ammonia present was then determined by titration with a standard solution of hydrochloric acid. The pH of the gastrointestinal fluid was measured using a pH test paper and the size and form of the microemulsions were examined under the microscope. 18 healthy rats were randomly divided into three groups, fasted for 24 hours and allowed to drink normally. Three-way pipes were placed at the gastroduodenal junction in Group I, and at the terminal ileum in Group II. After the intragastric administration of ME-RCA, the stomach contents in Group I, the effluent from the terminal ileum in Group II and discharge from the anus in Group III were collected. The pH values of the gastrointestinal juice were measured by the pH test paper and those of the colon were determined by a universal indicator. These animal experiments were also used to test the effect of MBG-RCA.</p> <p>Results</p> <p>MBG-RCA showed a better removal rate of artificial colonic ammonia than ME-RCA (P < 0.05). The decrease in pH value of the artificial small intestinal fluid due to ME-RCA did not occur when MBG-RCA was used. In the simulated gastrointestinal process, MBG-RCA maintained greater stability and released the emulsion (ME-RCA) in the colonic fluid. In the gastrointestinal tract of normal SD rats, ME-RCA decreased in size and lost its stable form after entering the small intestine, while MBG-RCA remained stable and intact emulsion-drops were observed from the anus. Neither substance had any effect on the pH of the stomach or colon of normal rats (partly because normal rats were fasted for 24 hours and allowed to drink normally, which resulted in a low level of ammonia production in the colon). Unlike ME-RCA, MBG-RCA did not reduce the pH of the small intestine.</p> <p>Conclusions</p> <p>MBG-RCA was more stable in the gastrointestinal tract and more effective at removing colonic ammonia when a higher concentration of ammonia was present. This made it possible to achieve the targeted removal of colonic ammonia and is a promising method to prevent hepatic encephalopathy (HE) in future studies.</p