When we face patients arriving to a hospital suffering from the effects of some illness, one of the main problems we can encounter is evaluating whether or not said patients are going to require intensive care in the near future. This intensive care requires allotting valuable and scarce resources, and knowing beforehand the severity of a patients illness can improve both its treatment and the organization of resources. We illustrate this issue in a dataset consistent of Spanish COVID-19 patients from the sixth epidemic wave where we label patients as critical when they either had to enter the intensive care unit or passed away. We then combine the use of dynamic Bayesian networks, to forecast the vital signs and the blood analysis results of patients over the next 40 hours, and neural networks, to evaluate the severity of a patients disease in that interval of time. Our empirical results show that the transposition of the current state of a patient to future values with the DBN for its subsequent use in classification obtains better the accuracy and g-mean score than a direct application with a classifier.
Machine Learning-Based COVID-19 Patients Triage Algorithm using Patient-Generated Health Data from Nationwide Multicenter Database
Pohang University of Science and Technology（浦项科技大学）；Basic Science Research Institute, Pohang University of Science and Technology（浦项科技大学基础科学研究所）；Seoul National University Bundang Hospital（首尔大学盆唐医院）
CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data
Universidade Federal do Rio de Janeiro（巴西里约热内卢联邦大学）；Programa de Pós-graduação Multicêntrico em Ciências Fisiológicas（多中心生理科学硕士课程）；NUPEM / UFRJ - Instituto de Biodiversidade e Sustentabilidade（NUPEM/UFRJ - 生物多样性与可持续发展研究所）；Fundação Oswaldo Cruz (Fiocruz)（奥斯沃阿尔多·克鲁兹基金会）；Programa de Pós-Graduação Stricto sensu em Biologia Computacional e Sistemas（严格意义上的计算生物学和系统硕士课程）；Universidade Federal do Rio de Janiero（巴西里约热内卢联邦大学）
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）
Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Asan Medical Center（峨山医院）；University of Ulsan College of Medicine（蔚山大学医学院）；College of Medicine, Chungnam National Univerity（忠南国立大学医学院）；College of Medicine, Yeungnam University（岭南大学医学院）；School of Medicine, Kyungpook National University（庆北国立大学医学院）
Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
Nanjing University（南京大学）；National Institute of Healthcare Data Science at Nanjing University（南京大学医疗健康数据科学国家研究院）；Central South University（中南大学）；Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；University of North Carolina（北卡罗来纳大学）
Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data
Bangabandhu Sheikh Mujibur Rahman Science & Technology University（班加班杜谢赫穆吉布·拉赫曼科技大学）；University of Rajshahi（拉杰沙希大学）；University of Technology Sydney（悉尼科技大学）；The University of Sydney（悉尼大学）；Jatiya Kabi Kazi Nazrul Islam University（贾蒂亚·卡比·卡齐·纳兹鲁尔·伊斯兰大学）；Imam Mohammad Ibn Saud Islamic University（伊玛目穆罕默德·伊本·沙特伊斯兰大学）；University of New South Wales（新南威尔士大学）；Deakin University（迪肯大学）；The Garvan Institute of Medical Research（加尔文医学研究所）
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Shandong First Medical University（山东第一医科大学）；Shandong Academy of Medical Sciences（山东省医学科学院）；Shandong Cancer Hospital and Institute（山东省肿瘤医院）
COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Stony Brook School of Medicine（石溪大学医学院）；Montefiore Medical Center（蒙蒂菲奥里医疗中心）；Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院）
Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Asan Medical Center, University of Ulsan College of Medicine（蔚山大学医学院峨山医学中心）；College of Medicine, Yeungnam University（岭南大学医学院）；School of Medicine, Kyungpook National University（庆北国立大学医学院）
Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression
Massachusetts Institute of Technology（麻省理工学院）；School of Communication Sciences and Disorders University of Central Florida（中央佛罗里达大学通信科学与障碍学系）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Center for Laryngeal Surgery and Voice Rehabilitation（嗓音外科与嗓音康复中心）；Department of Surgery（外科部门）；Institute of Health Professions（健康专业研究所）；Athinoula Martinos Center（阿提尼奥拉马丁诺斯中心）
MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images
National Institute of Technology Karnataka（卡纳塔克邦国立理工学院）；Kasturba Medical College and Hospital, Manipal（马尼帕尔卡斯图巴医学院及医院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；J.S.S. Medical College, Mysore（迈索尔J.S.S.医学院）；JSS Academy of Higher Education and Research（JSS高等教育与研究学院）
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task
HY Medical Technology（汇医慧影）；Coventry University（考文垂大学）；University of Exeter（埃克塞特大学）；Hospital of PLA（解放军910医院）；Fifth Affiliated Hospital of Sun Yat-sen University（中山大学附属第五医院）；Guangdong Provincial Key Laboratory of Biomedical Imaging（广东省生物医学影像重点实验室）；University of Florida（佛罗里达大学）
机器学习模型的黑盒性质阻碍了一些高精度模型在医学诊断中的部署。把自己的生命交给医学研究人员并不完全理解的模型是有风险的。然而，通过模型解释，黑箱模型可以及时揭示由于COVID-19大流行中感染患者激增而可能被医学从业者忽视的重要生物标志物。这项研究利用了2020年1月18日至2020年3月5日期间在中国珠海进行的92例确诊SARS-CoV-2实验室检测患者的数据库，以确定指示严重程度预测的生物标志物。通过使用置换特征重要性、部分依赖图（PDP）、个体条件期望（ICE）、累积局部效应（ALE）、局部可解释模型不可知解释（LIME）和Shapley加法解释（SHAP）来解释四种机器学习模型、决策树、随机森林、梯度提升树和神经网络，我们发现N末端脑钠肽前体（NTproBNP）、C-反应蛋白（CRP）和乳酸脱氢酶（LDH），淋巴细胞（LYM）减少与严重感染和死亡风险增加相关，这与最近对COVID-19的医学研究和其他使用专用模型的研究一致。我们进一步验证了我们的方法在一个大型开放数据集上的5644例确诊患者，这些患者来自巴西圣保罗的Israelita Albert Einstein医院，来自Kaggle，并揭示了白细胞，嗜酸性粒细胞和血小板作为COVID-19的三个指示性生物标志物。
Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data
City University of Hong Kong（香港城市大学）；University of Sydney（悉尼大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Southern California（南加利福尼亚大学）
Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images
University of the Basque Country（巴斯克大学）；IKERBASQUE；Ho Chi Minh City Open University（胡志明市开放大学）；University of Strasbourg（斯特拉斯堡大学）；INSERM（法国国家健康与医学研究院）；Université de Haute-Alsace（上阿尔萨斯大学）
本文提出 ViTReg-IP，一种由 ViT 与回归头构成的轻量模型，用于根据胸部 X 光量化 COVID-19 等肺病严重程度，并在多来源数据集上验证其低成本、高性能与泛化性。
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）
Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19
Bangabandhu Sheikh Mujibur Rahman Science and Technology University（班加班杜谢赫穆吉布·拉赫曼科技大学）；Khulna University（库尔纳大学）；Jatiya Kabi Kazi Nazrul Islam University（贾蒂亚·卡比·卡齐·纳兹鲁尔·伊斯兰大学）；University of Canberra（堪培拉大学）；Nagasaki University（长崎大学）；The Garvan Institute of Medical Research（加尔文医学研究所）；Royal North Shore Hospital SERT Institute（皇家北岸医院SERT研究所）；University of New South Wales（新南威尔士大学）；World Health Organization (WHO) Centre on eHealth（世界卫生组织电子健康中心）
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
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）
Predicting Trajectories of Long COVID in Adult Women: The Critical Role of Causal Disentanglement
National Library of Medicine（国家医学图书馆）；Stevens Institute of Technology（史蒂文斯理工学院）；Columbia University（哥伦比亚大学）；The George Washington University（乔治华盛顿大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
