Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees
Università degli Studi di Brescia（布雷西亚大学）；ASST Spedali Civili di Brescia（布雷西亚市民医院）
Alfonso Emilio Gerevini, Roberto Maroldi, Matteo Olivato, Luca Putelli, Ivan Serina
备注：
5th International Workshop on Knowledge Discovery in Healthcare Data (KDH) at ECAI 2020, mortality prediction, COVID-19
发表信息：
Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence (ECAI 2020)
AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In particular we address the task of predicting the risk of death of a patient at different times of the hospitalisation, on the base of some demographic information, chest X-ray scores and several laboratory findings. Our machine learning models use ensembles of decision trees trained and tested using data from more than 2000 patients. An experimental evaluation of the models shows good performance in solving the addressed task.
Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
Concordia Institute for Information Systems Engineering (CIISE)（康考迪亚信息系统工程研究所）；Concordia University（康考迪亚大学）；Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）；University of Toronto（多伦多大学）；McGill University Health Center-Research Institute（麦吉尔大学健康中心研究所）；University of Calgary（卡尔加里大学）
Prognosis of COVID-19 using Artificial Intelligence: A Systematic Review and Meta-analysis
ITU/WHO Focus Group AI on Health（国际电信联盟/世界卫生组织健康人工智能焦点组）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Sultan Qaboos University（苏丹卡布斯大学）；University of British Columbia（不列颠哥伦比亚大学）
Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
Unit of Computer Systems \& Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy College of Computer Science；Software Engineering, Shenzhen University, Shenzhen, China Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rays
G.B. Pant Government Engineering College（G.B. Pant政府工程学院）
Universal Laboratory Model: prognosis of abnormal clinical outcomes based on routine tests
Department of medicinal chemistry Moscow State University（药物化学系莫斯科国立大学）；Department of computer science Samara National Research University（计算机科学系萨马拉国立研究大学）
Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Research Institute（BC癌症研究所）；TECVICO Corp.（TECVICO公司）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Nooshirvani University of Technology（努什拉尼理工大学）；Guilan University of Medical Sciences（吉兰医科大学）；Motamed Cancer Institute（莫塔梅德癌症研究所）；Hamadan University of Medical Sciences（哈马丹医科大学）
COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics
National Research Council Canada（加拿大国家研究委员会）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；Oakville Trafalgar Memorial Hospital（奥克维尔特拉法加纪念医院）；McMaster University（麦克马斯特大学）
COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing
Khulna University of Engineering & Technology（库尔纳工程技术大学）；University Health Network（大学健康网络）；University of Toronto（多伦多大学）
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Georgia Institute of Technology（佐治亚理工学院）；Mayo Clinic Arizona（妙佑医疗国际亚利桑那州院区）；Arizona State University（亚利桑那州立大学）；Columbia University Medical Center（哥伦比亚大学医学中心）
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Royal Brompton Hospital（皇家布朗普顿医院）；Shenzhen Second People’s Hospital（深圳市第二人民医院）；TECNALIA（泰克纳利亚研究中心）；University of the Basque Country（巴斯克大学）
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
University of Cambridge（剑桥大学）；AstraZeneca（阿斯利康）；University of Manchester（曼彻斯特大学）；Royal Papworth Hospital（皇家帕普沃斯医院）；Cambridge University Hospitals NHS Trust（剑桥大学医院国民医疗服务信托）
Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
East China Normal University（华东师范大学）；Ryerson University（瑞尔森大学）；Shanghai Jianglai Data Technology Co., Ltd（上海江来数据科技有限公司）
研究意义：本文的扩展版本已被IEEE Internet of Things期刊（DOI：10.1109/JIOT.2020.2991456）接受，请引用期刊版本。在疫情防控期间，我们的研究有助于基于呼吸特征的COVID-19（新型冠状病毒）感染患者的预后、诊断和筛查。根据最新的临床研究，COVID-19的呼吸模式与流感和普通感冒的呼吸模式不同。COVID-19中发生的一个重要症状是呼吸急促。感染COVID-19的人呼吸更快。本研究可用于各种呼吸模式的识别，并可初步投入实用。该方法在单受试者和双受试者情况下的演示视频可以在线下载。研究详情：以远程和不显眼的方式准确检测人的意外异常呼吸模式具有重要意义。在这项工作中，我们创新地利用深度相机和深度学习来实现这一目标。这项任务的挑战是双重的：真实世界的数据量不足以训练获得深度模型;不同类型的呼吸模式的类内变化很大，类外变化很小。本文针对呼吸信号的特点，提出了一种新颖高效的呼吸仿真模型（Respiratory Simulation Model，简称RSM），以弥补训练数据量大和真实数据量少的不足。所提出的深度模型和建模思想具有很大的潜力，可以扩展到大规模的应用，如公共场所，睡眠场景，办公环境。
Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images
Centre for Computational Intelligence (CCI), De Montfort University（德蒙福特大学计算智能中心）；Instituto Tecnologico de Costa Rica（哥斯达黎加理工学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机科学学院）；Imágenes Médicas Dr Chavarría Estrada（查瓦里亚·埃斯特拉达医学影像中心）；Biomedic Research Institute of Málaga (IBIMA)（马拉加生物医学研究所）
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
ARTORG Center for Biomedical Engineering Research（ARTORG生物医学工程研究中心）；University of Bern（伯尔尼大学）；Bern University Hospital（伯尔尼大学医院）；Lindenhofspital（林登霍夫医院）；Campus Stiftung Lindenhof Bern（伯尔尼林登霍夫基金会校区）
AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study
University Campus Bio-Medico of Rome（罗马生物医学自由大学）；Centro Diagnostico Italiano S.p.A.（意大利诊断中心股份公司）；Istituto Italiano di Tecnologia（意大利理工学院）；Bracco Imaging S.p.A.（博莱科影像股份公司）；Sapienza University of Rome（罗马大学）；Fondazione IRCCS Policlinico San Matteo（圣马泰奥综合医院IRCCS基金会）
Estimating COVID-19 cases and outbreaks on-stream through phone-calls
International Center for Advanced Studies (ICAS)（国际高级研究中心（ICAS））；ICIFI-CONICET；ECyT-UNSAM；Ministerio de Salud de la Provincia de Buenos Aires（布宜诺斯艾利斯省卫生部）
COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning
Concordia University（康考迪亚大学）；McGill University Health Center-Research Institute（麦吉尔大学健康中心研究所）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）；University of Montreal（蒙特利尔大学）；Iran University of Medical Science（伊朗医科大学）
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Inception Institute of Artificial Intelligence（人工智能研究院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models
College of Science, University of Al-Qadisiyah（卡迪西亚大学理学院）；Liverpool John Moors University（利物浦约翰摩尔大学）；Specialist in Internal Medicine - Pulmonary Disease（内科及肺科专科医师机构）
