In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task. In this work, we introduce a novel multi-instance neural network, AMI-Net+, to train and predict from the incomplete and extremely imbalanced data. It is more effective than the state-of-art method, AMI-Net. First, we also implement embedding, multi-head attention and gated attention-based multi-instance pooling to capture the relations of symptoms themselves and with the given disease. Besides, we propose var-ious improvements to AMI-Net, that the cross-entropy loss is replaced by focal loss and we propose a novel self-adaptive multi-instance pooling method on instance-level to obtain the bag representation. We validate the performance of AMI-Net+ on two real-world datasets, from two different medical domains. Results show that our approach outperforms other base-line models by a considerable margin.
National Research University Higher School of Economics（国立研究型大学高等经济学院）；Laboratory of Methods for Big Data Analysis（大数据分析方法实验室）
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches.
School of Computing Science, Newcastle University（纽卡斯尔大学计算科学学院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Merck（默克公司）；Bioscience Institute, Newcastle University（纽卡斯尔大学生物科学研究所）；Institut de Recherches Internationales Servier（赛诺菲国际研究所）；GlaxoSmithKline（葛兰素史克公司）；Delft University of Technology（代尔夫特理工大学）；State Research Institute Centre for Innovative Medicine（创新医学国家研究中心）；University of Oulu（奥卢大学）；Versus Arthritis（对抗关节炎组织）
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class class…
The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK Department for Transport, which are drastically imbalanced with missing attributes sometimes approaching 50\% of the overall data dimensionality. The paper presents the data analysis pipeline starting from the publicly available data of road traffic accidents and ending with predictors of possible injuries and their degree of severity. It addresses the huge …
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））；School of Computing & Information Sciences, Caritas Institute of Higher Education（明爱专上学院计算与信息科学学院）
With the proliferation of IoT devices, the distributed control systems are now capturing and processing more sensors at higher frequency than ever before. These new data, due to their volume and novelty, cannot be effectively consumed without the help of data-driven techniques. Deep learning is emerging as a promising technique to analyze these data, particularly in soft sensor modeling. The strong representational capabilities of complex data and the flexibility it offers from an architectural perspective make it a topic of active applied research in industrial settings. However, the successf…
Enhancing stop location detection for incomplete urban mobility datasets
IMT School for Advanced Studies Lucca（卢卡IMT高等研究院）；Carlos III University of Madrid（马德里卡洛斯三世大学）；Caja Blanca Datos SL（Caja Blanca Datos SL公司）；University of Amsterdam（阿姆斯特丹大学）；University of Zaragoza（萨拉戈萨大学）；University of Padua（帕多瓦大学）；Cuebiq Inc.（Cuebiq公司）
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Computer Engineering Department, İstanbul Commerce University（伊斯坦布尔商业大学计算机工程系）；Turkcell Teknoloji（Turkcell技术公司）；Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory（橡树岭国家实验室电网安全与能源基础设施部门）
针对联邦学习中的噪声标签、类别缺失与不均衡问题，提出结合自适应噪声清洗、条件 GAN 合成补全和鲁棒训练的隐私保护框架，并在 MNIST 与 Fashion-MNIST 上验证其性能提升。
The University of Sydney（悉尼大学）；Renmin University of China（中国人民大学）；Beijing Medicinovo Technology Co.,Ltd.（北京美迪诺沃科技有限公司）
One way to extract patterns from clinical records is to consider each patient record as a bag with various number of instances in the form of symptoms. Medical diagnosis is to discover informative ones first and then map them to one or more diseases. In many cases, patients are represented as vectors in some feature space and a classifier is applied after to generate diagnosis results. However, in many real-world cases, data is often of low-quality due to a variety of reasons, such as data consistency, integrity, completeness, accuracy, etc. In this paper, we propose a novel approach, attentio…
