Recently, deep learning has been successfully applied to unsupervised active learning. However, the current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving huge room to design more effective representation learning mechanisms for unsupervised active learning. In this brief, we propose a novel deep unsupervised active learning model via learnable graphs, named ALLGs. ALLG benefits from learning optimal graph structures to acquire better sample representation and select representative samples. To make the learned graph structure more stable and effective, we take into account k -nearest neighbor graph as a priori and learn a relation propagation graph structure. We also incorporate shortcut connections among different layers, which can alleviate the well-known over-smoothing problem to some extent. To the best of our knowledge, this is the first attempt to leverage graph structure learning for unsupervised active learning. Extensive experiments performed on six datasets demonstrate the efficacy of our method.

In recent years, machine learning methods have been widely used in modern electronic health record (EHR) systems, and have shown more accurate prediction performance on disease risk assessment tasks than traditional methods. However, most of the existing machine learning methods make the assessment solely based on features of the target case but ignore the cross-sample feature interactions between the target case and other similar cases, which is inconsistent with the general practice of evidence-based medicine of making diagnoses based on existing clinical experience. Moreover, current methods that focus on mining cross-sample information rely on deep neural networks to extract cross-sample feature interactions, which would suffer from the problems of data insufficiency, data heterogeneity and lack of interpretability in disease risk assessment tasks. In this work, we propose a novel retrieval-based gradient boosting decision trees (RB-GBDT) model with a cross-sample extractor to mine cross-sample information while exploiting the superiority of GBDT of robustness, generalization and interpretability. Experiments on real-world clinical datasets show the superiority and efficacy of RB-GBDT on disease risk assessment tasks. The developed software has been deployed in hospital as an auxiliary diagnosis tool for risk assessment of venous thromboembolism.

Venous thromboembolism (VTE) is the world's third most common cause of vascular mortality and a serious complication from multiple departments. Risk assessment of VTE guides clinical intervention in time and is of great importance to in-hospital patients. Traditional VTE risk assessment methods based on scaling tools, which always require rules carefully designed by human experts, are difficult to apply to large-population scenarios since the manually designed rules are not guaranteed to be accurate to all populations. In contrast, with the development of the electronic health record (EHR) datasets, data-driven machine-learning-based risk assessment methods have proven superior predictability in many studies in recent years. This paper uses the gradient boosting tree model to study the VTE risk assessment problem with multi-department data. There exist two distinct characteristics of VTE data collected at the level of the entire hospital: its wide distribution and heterogeneity across multiple departments. To this end, we consider the prediction task over multiple departments as a multi-task learning process, and introduce the algorithm of a task-aware tree-based method TSGB to tackle the multi-task prediction problem. Although the introduction of multi-task learning improves overall across-department performance, we reveal the problem of task-wise performance decline while dealing with imbalanced VTE data volume. According to the analysis, we finally propose two variants of TSGB to alleviate the problems and further boost the prediction performance. Compared with state-of-the-art rule-based and multi-task tree-based methods, the experimental results show the proposed methods not only improve the overall across-department AUC performance effectively, but also ensure the improvement of performance over every single department prediction.

Unsupervised active learning has become an active research topic in the machine learning and computer vision communities, whose goal is to choose a subset of representative samples to be labeled in an unsupervised setting. Most of existing approaches rely on shallow linear models by assuming that each sample can be well approximated by the span (i.e., the set of all linear combinations) of the selected samples, and then take these selected samples as the representative ones for manual labeling. However, the data do not necessarily conform to the linear models in many real-world scenarios, and how to model nonlinearity of data often becomes the key point of unsupervised active learning. Moreover, the existing works often aim to well reconstruct the whole dataset, while ignore the important cluster structure, especially for imbalanced data. In this paper, we present a novel deep unsupervised active learning framework. The proposed method can explicitly learn a nonlinear embedding to map each input into a latent space via a deep neural network, and introduce a selection block to select the representative samples in the learnt latent space through a self-supervised learning strategy. In the selection block, we aim to not only preserve the global structure of the data, but also capture the cluster structure of the data in order to well handle the data imbalance issue during sample selection. Meanwhile, we take advantage of the clustering result to provide self-supervised information to guide the above processes. Finally, we attempt to preserve the local structure of the data, such that the data embedding becomes more precise and the model performance can be further improved. Extensive experimental results on several publicly available datasets clearly demonstrate the effectiveness of our method, compared with the state-of-the-arts.

Venous thromboembolism (VTE) is a common vascular disease and potentially fatal complication during hospitalization, and so the early identification of VTE risk is of significant importance. Compared with traditional scale assessments, machine learning methods provide new opportunities for precise early warning of VTE from clinical medical records. This research aimed to propose a two-stage hierarchical machine learning model for VTE risk prediction in patients from multiple departments. First, we built a machine learning prediction model that covered the entire hospital, based on all cohorts and common risk factors. Then, we took the prediction output of the first stage as an initial assessment score and then built specific models for each department. Over the duration of the study, a total of 9213 inpatients, including 1165 VTE-positive samples, were collected from four departments, which were split into developing and test datasets. The proposed model achieved an AUC of 0.879 in the department of oncology, which outperformed the first-stage model (0.730) and the department model (0.787). This was attributed to the fully usage of both the large sample size at the hospital level and variable abundance at the department level. Experimental results show that our model could effectively improve the prediction of hospital-acquired VTE risk before image diagnosis and provide decision support for further nursing and medical intervention.

Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating. Most existing works are based on shallow linear models by assuming that each sample can be well approximated by the span (i.e., the set of all linear combinations) of certain selected samples, and then take these selected samples as representative ones to label. However, in practice, the data do not necessarily conform to linear models, and how to model nonlinearity of data often becomes the key point to success. In this paper, we present a novel Deep neural network framework for Unsupervised Active Learning, called DUAL. DUAL can explicitly learn a nonlinear embedding to map each input into a latent space through an encoder-decoder architecture, and introduce a selection block to select representative samples in the the learnt latent space. In the selection block, DUAL considers to simultaneously preserve the whole input patterns as well as the cluster structure of data. Extensive experiments are performed on six publicly available datasets, and experimental results clearly demonstrate the efficacy of our method, compared with state-of-the-arts.

Temporal action proposal generation is an active topic in computer vision and image/video processing communities, which aims to predict a set of temporal proposals to discover all action instances with high recall and intersection over union (IoU) in real-world untrimmed videos. Many previous approaches rely on the two-stream network which intends to simultaneously extract spatial (appearance) and temporal features for representing each video. However, few work considers to capture the correlations between the two kinds of features, leaving a large room for improving the model. In this paper, we present a new method for generating temporal action proposals based on multi-task feature learning. Specifically, we aim to learn shared representation between the spatial and temporal features in a multi-task learning framework, so as to acquire a compact and precise feature representation. Moreover, we devise a correlation loss to address the `weak-correlation' problem with high IoUs but low confidences cores. Finally, we take an ensemble learning strategy in order to inherit the advantages of existing works. Extensive experimental results on the ActivityNet-1.3 challenge dataset show that the proposed method achieves the best performance, compared with the state-of-the-arts reported in the literature and the official leaderboard. Our code will be released soon.