Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.Comment: 8 pages, this paper has been accepted by AAAI 201

HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding

International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns unique International Classification of Diseases codes with each medical record, is widely used recently for its efficiency and error-prone avoidance. However, there are challenges that remain such as heterogeneity, label unbalance, and complex relationships between ICD codes. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes' hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction. We validate our method on two widely used datasets. Experimental results on two authoritative public datasets demonstrate that our proposed method boosts state-of-the-art performance by a large margin

Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via Collaborative Multi-expert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines.Comment: Accepted by IEEE Transactions on Big Data (TBD 2024

Numerical simulation method study of rock fracture based on strain energy density theory

Many numerical methods are carried out to study the nonlinear failure behaviors of the rock; however, the numerical simulation methods for the failed rock are still in the research stage. This paper establishes the damage constitutive equation by combining the bilinear strain softening constitutive model with energy dissipation principles, as well as the energy failure criterion of mesoscopic elements based on the strain energy density theory. When the strain energy stored by an element exceeds a fixed value, the element enters the damage state and the damage degree increases with increasing energy dissipation. Simultaneously, the material properties of the damaged element change until it becomes an element with certain residual strength. As the load increases, the damage degree of an element increases. When the strain energy stored by an element exceeds the established value of the energy criterion, the element is defined to be failed. As the number of failed elements constantly increases, failed elements interconnect and form macrocracks. The rock fracture calculation program on the basis of the preceding algorithm is successfully applied to the fracture simulation process in Brazilian splitting, tensile tests with build-in crack and tunnel excavation

Study on the effect of new type liquid accelerator on the performance of shotcrete

Shotcrete is an essential preliminary support means in New Austrian Tunneling Method (NATM) construction and plays a very important role in controlling the stability of surrounding rock. The accelerator is a necessary admixture in shotcrete and its quality can greatly affect shotcrete performance. This paper proposes a new liquid accelerator characterized by short initial and final setting time, small dosage, and good adaptability to cement. Laboratory tests and field tests are conducted to verify the influence of this liquid accelerator on performance of shotcrete. Numerical simulation is carried out to study the strength growth of shotcrete with time and interaction between the strength and stress release of surrounding rock. The results show that the initial and final setting time of this liquid accelerator is 2 minutes and 4 minutes respectively. Its dosage is just 1.5% to 4% of the cement quantity. Adding this liquid accelerator can effectively improve the early strength and reduce the later strength loss of shotcrete, and therefore enhance the supporting effects of shotcrete on surrounding rock. In the field application, it is an ideal liquid accelerator for shotcrete, characterized by little resilience, no slurry shedding, and low dust

Numerical Simulation Method Study of Rock Fracture Based on Strain Energy Density Theory

Many numerical methods are carried out to study the nonlinear failure behaviors of the rock; however, the numerical simulation methods for the failed rock are still in the research stage. This paper establishes the damage constitutive equation by combining the bilinear strain softening constitutive model with energy dissipation principles, as well as the energy failure criterion of mesoscopic elements based on the strain energy density theory. When the strain energy stored by an element exceeds a fixed value, the element enters the damage state and the damage degree increases with increasing energy dissipation. Simultaneously, the material properties of the damaged element change until it becomes an element with certain residual strength. As the load increases, the damage degree of an element increases. When the strain energy stored by an element exceeds the established value of the energy criterion, the element is defined to be failed. As the number of failed elements constantly increases, failed elements interconnect and form macrocracks. The rock fracture calculation program on the basis of the preceding algorithm is successfully applied to the fracture simulation process in Brazilian splitting and intermediate crack tensile tests