In this work, we present a novel trajectory comparison algorithm to identify abnormal vital sign trends, with the aim of improving recognition of deteriorating health.
There is growing interest in continuous wearable vital sign sensors for monitoring patients remotely at home. These monitors are usually coupled to an alerting system, which is triggered when vital sign measurements fall outside a predefined normal range. Trends in vital signs, such as increasing heart rate, are often indicative of deteriorating health, but are rarely incorporated into alerting systems.
We introduce a dynamic time warp distance-based measure to compare time series trajectories. We split each multi-variable sign time series into 180 minute, non-overlapping epochs. We then calculate the distance between all pairs of epochs. Each epoch is characterized by its mean pairwise distance (average link distance) to all other epochs, with clusters forming with nearby epochs.
We demonstrate in synthetically generated data that this method can identify abnormal epochs and cluster epochs with similar trajectories. We then apply this method to a real-world data set of vital signs from 8 patients who had recently been discharged from hospital after contracting COVID-19. We show how outlier epochs correspond well with the abnormal vital signs and identify patients who were subsequently readmitted to hospital.
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies
Chittagong University of Engineering & Technology（吉大港工程技术大学）；Jahangirnagar University（贾汉吉尔纳加尔大学）；Canberra Institute of Technology（堪培拉理工学院）；Kuwait University（科威特大学）
Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study
Huma Therapeutics Limited（Huma Therapeutics有限公司）；Imperial College London（帝国理工学院）；University of Belgrade（贝尔格莱德大学）；University of Exeter（埃克塞特大学）；Johns Hopkins Bloomberg School Public Health（约翰斯·霍普金斯大学彭博公共卫生学院）
Privacy-aware Early Detection of COVID-19 through Adversarial Training
CVSSP, University of Surrey（萨里大学视觉、语音与信号处理中心）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；UK Dementia Research Institute Care Research and Technology Centre, Imperial College London（伦敦帝国理工学院英国痴呆症研究所护理研究与技术中心）；University of Surrey（萨里大学）；Queen Mary University of London（伦敦玛丽女王大学）；Oxford-Suzhou Centre for Advanced Research（牛津大学苏州高等研究院）；John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust（牛津大学医院NHS基金会信托约翰·拉德克利夫医院）；RDM Division of Cardiovascular Medicine, University of Oxford（牛津大学拉德克利夫医学系心血管医学部）
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE
Institute of Hospital and Health Care Administration（医院与健康照护管理研究所）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Far Eastern Memorial Hospital（远东纪念医院）
An Efficient Outlier Detection Algorithm for Data Streaming
California State University, San Bernardino（加州州立大学圣贝纳迪诺分校）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；University of California, Riverside（加州大学河滨分校）
Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network
University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；McGovern Medical School of The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心麦戈文医学院）
Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
University of Electronic Science and Technology of China（电子科技大学）；Sichuan University West China Hospital（四川大学华西医院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）
CovidLLM: A Robust Large Language Model with Missing Value Adaptation and Multi-Objective Learning Strategy for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学院）；School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）；Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院）
Hybrid Approach to Identify Druglikeness Leading Compounds against COVID-19 3CL Protease
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学研究所）
SARS-COV-2是一种基于单链RNA的阳性大分子，自2022年6月以来已导致超过630万人死亡。此外，通过封锁扰乱全球供应链，病毒间接对全球经济造成了毁灭性的破坏。设计和开发针对这种病毒及其各种变体的药物至关重要。在这篇论文中，我们开发了一个基于计算机模拟研究的混合框架，以重新利用现有的治疗药物来寻找能够治愈Covid-19的药物样生物活性分子。对从ChEMBL数据库中检索到的分子进行Lipinski规则分析，发现了133个抗SARS冠状病毒3CL蛋白酶类药物活性分子。根据标准IC 50，将数据集分为三个类别：活性、非活性和中间体。我们的比较分析表明，所提出的额外树回归（ETR）为基础的QSAR模型相比，梯度提升，XGBoost，支持向量机，决策树，和随机森林为基础的回归模型具有改善的预测结果有关的化合物的生物活性。进行ADMET分析以鉴定具有ChEMBL ID 187460、190743、222234、222628、222735、222769、222840、222893、225515、358279、363535、365134和426898的十三种生物活性分子。这些分子是非常适合SARS-COV-2 3CL蛋白酶的候选药物。在下一步骤中，使用分子对接根据结合亲和力计算生物活性分子的功效，然后将具有ChEMBL ID 187460、222769、225515、358279、363…
