A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl

Islet autoantibodies and residual beta cell function in type 1 diabetes children followed for 3-6 years

Aims: To test if islet autoantibodies at diagnosis of type 1 diabetes (T1DM) and after 3-6 years with T1D predict residual beta-cell function (RBF) after 3-6 years with T1D. Methods: T1D children (n = 260, median age at diagnosis 9.4, range 0.9-14.7 years) were tested for GAD65, IA-2, ZnT8R, ZnT8W and ZnT8Q autoantibodies (A) at diagnosis, and 3-6 years after diagnosis when also fasting and stimulated RBF were determined. Results: For every 1-year increase in age at diagnosis of TID, the odds of detectable C-peptide increased 1.21 (1.09, 1.34) times for fasting C-peptide and 1.28 (1.15, 1.42) times for stimulated C-peptide. Based on a linear model for subjects with no change in IA-2A levels, the odds of detectable C-peptide were 35% higher than for subjects whose IA-2A levels decreased by half (OR = 1.35 (1.09, 1.67), p = 0.006); similarly for ZnT8WA (OR = 1.39 (1.09, 1.77), p = 0.008) and ZnT8QA (OR = 1.55 (1.06, 2.26) p = 0.024). Such relationship was not detected for GADA or ZnT8RA. All OR adjusted for confounders. Conclusions: Age at diagnosis with T1D was the major predictor of detectable C-peptide 3-6 years post-diagnosis. Decreases in IA-2A, and possibly ZnT8A, levels between diagnosis and post-diagnosis were associated with a reduction in RBF post-diagnosis. (C) 2012 Elsevier Ireland Ltd. All rights reserved

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

Secondary Sex Ratio among Women Exposed to Diethylstilbestrol in Utero

BACKGROUND. Diethylstilbestrol (DES), a synthetic estrogen widely prescribed to pregnant women during the mid-1900s, is a potent endocrine disruptor. Previous studies have suggested an association between endocrine-disrupting compounds and secondary sex ratio. METHODS. Data were provided by women participating in the National Cancer Institute (NCI) DES Combined Cohort Study. We used generalized estimating equations to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the relation of in utero DES exposure to sex ratio (proportion of male births). Models were adjusted for maternal age, child's birth year, parity, and cohort, and accounted for clustering among women with multiple pregnancies. RESULTS. The OR for having a male birth comparing DES-exposed to unexposed women was 1.05 (95% CI, 0.95-1.17). For exposed women with complete data on cumulative DES dose and timing (33%), those first exposed to DES earlier in gestation and to higher doses had the highest odds of having a male birth. The ORs were 0.91 (95% C, 0.65-1.27) for first exposure at ≥ 13 weeks gestation to < 5 g DES; 0.95 (95% CI, 0.71-1.27) for first exposure at ≥ 13 weeks to ≥ 5 g; 1.16 (95% CI, 0.96-1.41) for first exposure at < 13 weeks to < 5 g; and 1.24 (95% CI, 1.04-1.48) for first exposure at < 13 weeks to ≥ 5 g compared with no exposure. Results did not vary appreciably by maternal age, parity, cohort, or infertility history. CONCLUSIONS. Overall, no association was observed between in utero DES exposure and secondary sex ratio, but a significant increase in the proportion of male births was found among women first exposed to DES earlier in gestation and to a higher cumulative dose.National Cancer Institute (N01-CP-21168, N01-CP-51017, N01-CP-01289

Hospital admissions for vitamin D related conditions and subsequent immune-mediated disease: record-linkage studies

PMCID: PMC3729414The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1741-7015/11/171. 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

Relation between falciparum malaria and bacteraemia in Kenyan children: a population-based, case-control study and a longitudinal study.

BACKGROUND: Many investigators have suggested that malaria infection predisposes individuals to bacteraemia. We tested this hypothesis with mendelian randomisation studies of children with the malaria-protective phenotype of sickle-cell trait (HbAS). METHODS: This study was done in a defined area around Kilifi District Hospital, Kilifi, Kenya. We did a matched case-control study to identify risk factors for invasive bacterial disease, in which cases were children aged 3 months to 13 years who were admitted to hospital with bacteraemia between Sept 16, 1999, and July 31, 2002. We aimed to match two controls, by age, sex, location, and time of recruitment, for every case. We then did a longitudinal case-control study to assess the relation between HbAS and invasive bacterial disease as malaria incidence decreased. Cases were children aged 0-13 years who were admitted to hospital with bacteraemia between Jan 1, 1999, and Dec 31, 2007. Controls were born in the study area between Jan 1, 2006, and June 23, 2009. Finally, we modelled the annual incidence of bacteraemia against the community prevalence of malaria during 9 years with Poisson regression. RESULTS: In the matched case-control study, we recruited 292 cases-we recruited two controls for 236, and one for the remaining 56. Sickle-cell disease, HIV, leucocyte haemozoin pigment, and undernutrition were positively associated with bacteraemia and HbAS was strongly negatively associated with bacteraemia (odds ratio 0·36; 95% CI 0·20-0·65). In the longitudinal case-control study, we assessed data from 1454 cases and 10,749 controls. During the study period, the incidence of admission to hospital with malaria per 1000 child-years decreased from 28·5 to 3·45, with a reduction in protection afforded by HbAS against bacteraemia occurring in parallel (p=0·0008). The incidence of hospital admissions for bacteraemia per 1000 child-years also decreased from 2·59 to 1·45. The bacteraemia incidence rate ratio associated with malaria parasitaemia was 6·69 (95% CI 1·31-34·3) and, at a community parasite prevalence of 29% in 1999, 62% (8·2-91) of bacteraemia cases were attributable to malaria. INTERPRETATION: Malaria infection strongly predisposes individuals to bacteraemia and can account for more than half of all cases of bacteraemia in malaria-endemic areas. Interventions to control malaria will have a major additional benefit by reducing the burden of invasive bacterial disease. FUNDING: Wellcome Trust

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Highly Conducting pi-Conjugated Molecular Junctions Covalently Bonded to Gold Electrodes

We measure electronic conductance through single conjugated molecules bonded to Au metal electrodes with direct Au-C covalent bonds using the scanning tunneling microscope based break-junction technique. We start with molecules terminated with trimethyltin end groups that cleave off in situ resulting in formation of a direct covalent sigma bond between the carbon backbone and the gold metal electrodes. The molecular carbon backbone used in this study consist of a conjugated pi-system that has one terminal methylene group on each end, which bonds to the electrodes, achieving large electronic coupling of the electrodes to the pi-system. The junctions formed with the prototypical example of 1,4-dimethylenebenzene show a conductance approaching one conductance quantum (G0 = 2e2/h). Junctions formed with methylene terminated oligophenyls with two to four phenyl units show a hundred-fold increase in conductance compared with junctions formed with amine-linked oligophenyls. The conduction mechanism for these longer oligophenyls is tunneling as they exhibit an exponential dependence of conductance with oligomer length. In addition, density functional theory based calculations for the Au-xylylene-Au junction show near-resonant transmission with a cross-over to tunneling for the longer oligomers.Comment: Accepted to the Journal of the American Chemical Society as a Communication

Study of the bivariate survival data using frailty models based on Lévy processes

Frailty models allow us to take into account the non-observable inhomogeneity of individual hazard functions. Although models with time-independent frailty have been intensively studied over the last decades and a wide range of applications in survival analysis have been found, the studies based on the models with time-dependent frailty are relatively rare. In this paper, we formulate and prove two propositions related to the identifiability of the bivariate survival models with frailty given by a nonnegative bivariate Lévy process. We discuss parametric and semiparametric procedures for estimating unknown parameters and baseline hazard functions. Numerical experiments with simulated and real data illustrate these procedures. The statements of the propositions can be easily extended to the multivariate case