Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction

Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive visits into account, where irregular time intervals, incorporated as auxiliary information, are fed into health prediction models to capture latent progressive patterns of patient journeys. We present a novel deep neural network with four modules to take into account the contributions of various variables for health prediction: i) the Stacked Attention module strengthens the deep semantics in clinical events within each patient journey and generates visit embeddings, ii) the Short-Term Temporal Attention module models short-term correlations between consecutive visit embeddings while capturing the impact of time intervals within those visit embeddings, iii) the Long-Term Temporal Attention module models long-term dependencies between visit embeddings while capturing the impact of time intervals within those visit embeddings, iv) and finally, the Coupled Attention module adaptively aggregates the outputs of Short-Term Temporal Attention and Long-Term Temporal Attention modules to make health predictions. Experimental results on MIMIC-III demonstrate superior predictive accuracy of our model compared to existing state-of-the-art methods, as well as the interpretability and robustness of this approach. Furthermore, we found that modeling short-term correlations contributes to local priors generation, leading to improved predictive modeling of patient journeys.Comment: 10 pages, 4 figures, accepted at ACM BCB 202

Where there is a will, there’s a way: Job search clarity, reemployment crafting and reemployment quality

Job hunting is regarded as a self-regulatory process. However, few studies have examined the mechanism underlying the job search goal-performance relationship from the perspective of the self-regulatory behavior of reemployment crafting (RC). Therefore, the purpose of this study was to examine the mediating role of RC in the relationship between job search clarity (JSC) and reemployment quality (RQ) and the moderating role of the reemployment context. A three-wave study was conducted among 295 rural migrant workers who had experienced unemployment to successful reemployment in China. Model 4 and Model 9 from SPSS macro PROCESS were used to test the moderated mediation model. The findings indicated that (1) JSC was positively correlated with RQ; (2) seeking resources (SR) and seeking challenging demands (SCD) fully mediated the relationship between JSC and RQ; (3) supportive environment (SEn) and challenging environment (CEn), independently, have moderating effect on the relationship between JSC and SR, as well as the relationship between JSC and SCD; and (4) the mediating effect of SR as well as SCD was significant and greater when SEn and CEn were both at high levels. This study contributes to goal-setting theory and highlights the important roles of RC and the reemployment context

Nonlinear System Dynamic Reliability Analysis Using Equivalent Duffing System Method

Equivalent linearization method is the main approach for nonlinear structural system random response analysis. But it will generate big error that using the random response results of equivalent linearization method to analyze the structural dynamic reliability. In order to improve the analysis precision of dynamic reliability of nonlinear system, an equivalent nonlinear system method is presented in this paper. In this method general nonlinear systems are converted to equivalent Duffing nonlinear system according to minimum mean square error principle, whose exact analytic solution of steady state of random responses can be worked out by Fokker Planck Kolmogorov equation (FPK equation). Then the exact results of stochastic response processes are used for the analysis of structural dynamic reliability. So it is not only convenient for calculation but also with high degree of accuracy for the results that using the equivalent nonlinear system method to analyze structural dynamic reliability. In addition, the equivalent nonlinear system adopted in this work has a parameterεwhich controls the degree of nonlinear. Thus we can obtain conveniently the analysis results of converting the original system to equivalent nonlinear systems with different degree of nonlinear by changing the value of the parameterε. In particular, when the parameter ε is equal to zero we can obtain the analysis results of equivalent linearization method. It is shown from the example analysis that the analysis results of equivalent nonlinear system method presented in this paper is reliable and the calculation accuracy is higher than equivalent linear system method apparently

Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist healthcare professionals in clinical decision-making. Existing methods focus on measuring the similarity between patients using deep neural networks to capture the hidden feature structures. However, the higher-order relationships are ignored, such as patient characteristics (e.g., diagnosis codes) and their causal effects on downstream clinical predictions. In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction. Evaluation using a publicly available eICU Collaborative Research Database indicates that our method achieves superior performance over the state-of-the-art models on mortality risk prediction. Moreover, the results of several case studies demonstrated the effectiveness of constructing graph networks in providing good transparency and robustness in decision-making.Comment: 7 pages, 2 figures, submitted to IEEE BIBM 202

ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint

Large-scale online recommender system spreads all over the Internet being in charge of two basic tasks: Click-Through Rate (CTR) and Post-Click Conversion Rate (CVR) estimations. However, traditional CVR estimators suffer from well-known Sample Selection Bias and Data Sparsity issues. Entire space models were proposed to address the two issues via tracing the decision-making path of "exposure_click_purchase". Further, some researchers observed that there are purchase-related behaviors between click and purchase, which can better draw the user's decision-making intention and improve the recommendation performance. Thus, the decision-making path has been extended to "exposure_click_in-shop action_purchase" and can be modeled with conditional probability approach. Nevertheless, we observe that the chain rule of conditional probability does not always hold. We report Probability Space Confusion (PSC) issue and give a derivation of difference between ground-truth and estimation mathematically. We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue. Specifically, we handle "exposure_click_in-shop action" and "in-shop action_purchase" separately in the light of characteristics of in-shop action. The first path is still treated with conditional probability while the second one is treated with parameter constraint strategy. Experiments on both offline and online environments in a large-scale recommendation system illustrate the superiority of our proposed methods over state-of-the-art models. The real-world datasets will be released