The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing resources, improving efficiency and scalability. Efficient IIoT operations require a trade-off between model quality and training latency. Consequently, a primary challenge of FL in IIoT is to optimize overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency for meta-computing. Additionally, the satisfaction function is incorporated into the utility function to incentivize IIoT nodes to participate in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for IIoT. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves utility by at least 23.7% compared to existing FL schemes without compromising model accuracy.

Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. Interest in health risk prediction has been increasing, especially with the availability of a large amount of electronic health records (EHR). An EHR contains multivariate time series data that records meaningful information associated with a chronological set of clinical events for each patient. Recurrent neural networks (RNN) and hidden Markov models (HMM) have been widely used as generative models of time series data. RNN-based models have strong prediction performance but lack transparency. HMMs have a simple functional form and the ability to provide an intuitive probabilistic interpretation, but their state dynamics are 'memoryless', making it difficult to thoroughly take into account the irregularity in patients' health trajectory. This paper proposes a novel deep Markov network for health risk prediction. The method integrates two modules, a GRU (Gated Recurrent Unit) with attention mechanism and a Neural HMM, into a single network. The GRU generates the inputs required for health risk predictions and uses an attention mechanism to create memorable state dynamics for the Neural HMM. The Neural HMM then provides interpretable structured representations through training. Mixture Density Networks are incorporated in the Neural HMM, which contribute to the modeling of complex patterns found in the transition process. Furthermore, an inference network is designed to embed hidden state representations of GRU and Neural HMM into the same space. The inference network enables the two types of representations to learn from each other during the decoding process of Neural HMM, thereby improving the quality of interpretable structured representations. Experimental results on MIMIC-III and eICU datasets demonstrate that our method can outperform state-of-the-art methods and provide transparency of the model decisions.

Electronic health records (EHRs) have an inherently high degree of irregularity, including many missing values and varying time intervals, due to variations in patient conditions and treatment needs. This makes successful health risk prediction challenging. EHRs contain longitudinal patient data that records meaningful information associated with a chronological set of clinical observations for each patient. Existing methods focus on modeling variable correlations in patient data with deep neural networks to impute missing values and feed complete data matrices into machine learning models to perform downstream healthcare prediction tasks. However, not enough attention was given to the reliability of the imputed values by these methods. Further, it is likely that the pattern of missing data in EHR contains important information affecting relationships among variables, including time intervals. We propose a novel deep imputation-prediction network to simultaneously perform imputation and prediction tasks with EHR. Our method has the advantages of being able to: 1) learn from the longitudinal patient data in both forward and backward directions, 2) generate both the predicted and imputed values and enhance the reliability of imputed values, and 3) incorporate three common decay functions to capture the variation pattern of input variables in time and adaptively enhances the temporal representation of each pattern with adjustable weights. As well, our method is able to examine the association between input variables to identify critical indicative variables regardless of how long ago the associated event happened. Experimental results on MIMIC-III and eICU datasets demonstrate the effectiveness and superiority of our method for both imputation and prediction, as well as transparency and interpretability, compared to existing state-of-the-art methods.

Predicting the health risks of patients based on electronic health records (EHRs) has recently attracted considerable research interest. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks of a specific health outcome can be used to support decisions by healthcare professionals. Various predictive models have been developed. Compared with traditional machine learning models, deep learning-based models have achieved more promising performance. However, due to the lack of transparency, the acceptance of deep learning-based models are often limited. This paper proposes a Stacked Attention-based Network, SANet, for accurate and interpretable health risk prediction. Two novel attention-based modules, named Convolutional Attention Module and Sequential Attention Module respectively, are designed to capture patient-specific contextual information at both feature and sequence levels. Particularly, Sequential Attention Module can flexibly learn the impact of the time interval between sequential visits and significantly enhance the interpretability and robustness of learning outcomes from sequences. Experimental results on two real-world EHR datasets demonstrate the superior predictive accuracy of our method, as well as interpretability and robustness, compared to existing state-of-the-art methods. The findings extracted by this approach are also empirically confirmed by relevant literature and medical experts.

Predicting the health risks of patients using Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks can be used to support decision-making by healthcare professionals. EHRs are structured patient journey data. Each patient journey contains a chronological set of clinical events, and within each clinical event, there is a set of clinical/medical activities. Due to variations of patient conditions and treatment needs, EHR patient journey data has an inherently high degree of missingness that contains important information affecting relationships among variables, including time. Existing deep learning-based models generate imputed values for missing values when learning the relationships. However, imputed data in EHR patient journey data may distort the clinical meaning of the original EHR patient journey data, resulting in classification bias. This paper proposes a novel end-to-end approach to modeling EHR patient journey data with Integrated Convolutional and Recurrent Neural Networks. Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation. Extensive experimental results using the proposed model on two real-world datasets demonstrate robust performance as well as superior prediction accuracy compared to existing state-of-the-art imputation-based prediction methods.

Electronic Health Records (EHRs) exhibit a high amount of missing data due to variations of patient conditions and treatment needs. Imputation of missing values has been considered an effective approach to deal with this challenge. Existing work separates imputation method and prediction model as two independent parts of an EHR-based machine learning system. We propose an integrated end-to-end approach by utilizing a Compound Density Network (CDNet) that allows the imputation method and prediction model to be tuned together within a single framework. CDNet consists of a Gated recurrent unit (GRU), a Mixture Density Network (MDN), and a Regularized Attention Network (RAN). The GRU is used as a latent variable model to model EHR data. The MDN is designed to sample latent variables generated by GRU. The RAN serves as a regularizer for less reliable imputed values. The architecture of CDNet enables GRU and MDN to iteratively leverage the output of each other to impute missing values, leading to a more accurate and robust prediction. We validate CDNet on the mortality prediction task on the MIMIC-III dataset. Our model outperforms state-of-the-art models by significant margins. We also empirically show that regularizing imputed values is a key factor for superior prediction performance. Analysis of prediction uncertainty shows that our model can capture both aleatoric and epistemic uncertainties, which offers model users a better understanding of the model results.

Autonomous moving objects (AMOs), such as automated guided vehicles (AGVs) and autonomous robots, have widely been used in the industry for decades. In an intelligent transport system with a great number of AMOs involved, it is important to eliminate potential congestion and deadlocks among AMOs to maintain a well-organized traffic flow. In this paper, we propose an algorithm that adapts bitonic merge sort algorithm for concurrent scheduling and routing of a great number (i.e., 4n2) of AMOs on an ntimesn mesh topology of path network without congestion or deadlocks among AMOs during their moves. The results are tested by experiments with randomly generated data and the comparison of a related model.