准确的预测模型对于分析新冠死亡趋势至关重要。本研究评估了自定义数据预处理流程对使用Our World in Data（OWID）数据的十种机器学习模型预测新冠死亡率的影响。我们的流程与标准预处理流程有四个关键不同之处。首先，它将每周报告的总数转换为每日更新，纠正报告偏差，提供更准确的估计。其次，它使用本地化异常检测和处理来保持数据方差并提高准确性。第三，它利用列之间的计算依赖性来确保数据一致性。最后，它采用迭代特征选择过程来优化特征集并提高模型性能。结果表明，自定义流程带来了显著改进：MLP回归器在测试集上的RMSE为66.556，R平方为0.991，超过了标准流程中的决策树回归器，其测试RMSE为222.858，R平方为0.817。这些发现强调了定制预处理技术在提高新冠死亡预测建模准确性方面的重要性。尽管针对本研究特定，这些方法为不同数据集和领域提供了有价值的见解，提高了各种情境下的预测性能。
英文摘要：
Accurate predictive models are crucial for analysing COVID-19 mortality trends. This study evaluates the impact of a custom data preprocessing pipeline on ten machine learning models predicting COVID-19 mortality using data from Our World in Data (OWID). Our pipeline differs from a standard preprocessing pipeline through four key steps. Firstly, it transforms weekly reported totals into daily updates, correcting reporting biases and providing more accurate estimates. Secondly, it uses localised outlier detection and processing to preserve data variance and enhance accuracy. Thirdly, it utilises computational dependencies among columns to ensure data consistency. Finally, it incorporates an iterative feature selection process to optimise the feature set and improve model performance. Results show a significant improvement with the custom pipeline: the MLP Regressor achieved a test RMSE of 66.556 and a test R-squared of 0.991, surpassing the DecisionTree Regressor from the standard pipeline, which had a test RMSE of 222.858 and a test R-squared of 0.817. These findings highlight the importance of tailored preprocessing techniques in enhancing predictive modelling accuracy for COVID-19 mortality. Although specific to this study, these methodologies offer valuable insights into diverse datasets and domains, improving predictive performance across various contexts.
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）
The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterio…
Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients
Imam Abdulrahman Bin Faisal University（伊玛目阿卜杜勒拉赫曼·本·费萨尔大学）；Ahsanullah University of Science and Technology（阿赫萨努拉科技大学）；Health Cluster（卫生集群）；Ajman University（阿治曼大学）
Qatar University（卡塔尔大学）；North South University（北南大学）；University of South Carolina（南卡罗来纳大学）；Hamad General Hospital（哈马德综合医院）；AFG College with the University of Aberdeen（阿伯丁大学AFG学院）
Fast and accurate detection of the disease can significantly help in reducing the strain on the healthcare facility of any country to reduce the mortality during any pandemic. The goal of this work is to create a multimodal system using a novel machine learning framework that uses both Chest X-ray (CXR) images and clinical data to predict severity in COVID-19 patients. In addition, the study presents a nomogram-based scoring technique for predicting the likelihood of death in high-risk patients. This study uses 25 biomarkers and CXR images in predicting the risk in 930 COVID-19 patients admitt…
The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interro…
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）
Background: Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus is a significant global challenge. Many individuals who become infected have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative about individual risk of severe illness and mortality. Accurately determining how comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. Methods: To assess…
Qatar University（卡塔尔大学）；University of Dhaka（达卡大学）；College of Medicine, QU Health, Qatar University（卡塔尔大学QU Health医学院）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to p…
Indian Institute of Technology, Delhi（印度理工学院德里分校）；AFMC（武装部队医学院）；All India Institute of Medical Sciences（全印度医学科学研究所）；Indian Institute of Science Education and Research, Bhopal（印度科学教育与研究院博帕尔分校）；Armed Forces Medical College（武装部队医学院）
Many countries are now experiencing the third wave of the COVID-19 pandemic straining the healthcare resources with an acute shortage of hospital beds and ventilators for the critically ill patients. This situation is especially worse in India with the second largest load of COVID-19 cases and a relatively resource-scarce medical infrastructure. Therefore, it becomes essential to triage the patients based on the severity of their disease and devote resources towards critically ill patients. Yan et al. 1 have published a very pertinent research that uses Machine learning (ML) methods to predict…
This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospital dataset to predict the mortality of COVID-19 patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and Locally Interpretable Model Agnostic Explanations (LIME), to compare the global and local interpretation of feature importance. This paper demonstrates the advantages of using XAI which shows the feature importance and decisi…
School of Information, The University of Texas at Austin（德克萨斯大学奥斯汀分校信息学院）；Dell Medical School, The University of Texas at Austin（德克萨斯大学奥斯汀分校戴尔医学院）
This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database. The dataset consists of 20,878 COVID-positive patients, among which 9,177 patients died in the year 2020. This paper aims to understand and interpret the association of socio-economic characteristics of patients with their mortality instead of maximizing prediction accuracy. According to our analysis, a patients households annual and disposable income, age, education, and employment status significantly impacts a machine learning models predic…
Indian Institute of Technology, Delhi（印度理工学院德里分校）；AFMC, Pune（武装部队医学院，浦那）；CMC Ludhiana（卢迪亚纳基督教医学院）
As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinica…
Disadvantaged groups around the world have suffered and endured higher mortality during the current COVID-19 pandemic. This contrast disparity suggests that socioeconomic and health-related factors may drive inequality in disease outcome. To identify these factors correlated with COVID-19 outcome, country aggregate data provided by the Lancet COVID-19 Commission subjected to correlation analysis. Socioeconomic and health-related variables were used to predict mortality in the top 5 most affected countries using ridge regression and extreme gradient boosting (XGBoost) models. Our data reveal th…
F. Hoffmann-La Roche Ltd（罗氏制药公司）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；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）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；MIT Critical Data, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Harvard-MIT Health Sciences & Technology（哈佛-麻省理工健康科学与技术研究所医学工程与科学研究所计算生理学实验室MIT关键数据）；University Hospital of Tübingen（蒂宾根大学医院）；Kantonsspital Winterthur（温特图尔州立医院）
Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients. Due to the exponential growth of infections, many healthcare systems across the world are under pressure to care for increasing amounts of at-risk patients. Given the high number of infected patients, identifying patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, we pre…
Caltech（加州理工学院）；Walter Burke Institute for Theoretical Physics（沃尔特·伯克理论物理研究所）
Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from …
A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities,…
Canadian VIGOUR Centre, University of Alberta（阿尔伯塔大学加拿大VIGOUR中心）；University of Alberta（阿尔伯塔大学）；Alberta Health Services（阿尔伯塔卫生服务局）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited av…
Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death. Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life. While early planning and surgical cannulation for ECMO can increase survival, clinicians report the lack of a risk score…
Erzincan Binali Yıldırım University（埃尔津詹比纳利·耶尔德勒姆大学）；Petrozavodsk State University（彼得罗扎沃茨克国立大学）
Early evaluation of patients who require special care and who have high death-expectancy in COVID-19, and the effective determination of relevant biomarkers on large sample-groups are important to reduce mortality. This study aimed to reveal the routine blood-value predictors of COVID-19 mortality and to determine the lethal-risk levels of these predictors during the disease process. The dataset of the study consists of 38 routine blood-values of 2597 patients who died (n = 233) and those who recovered (n = 2364) from COVID-19 in August-December, 2021. In this study, the histogram-based gradie…
At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study
University of Oxford（牛津大学）；Institute for Clinical Research, National Institutes of Health (NIH), Malaysia（马来西亚国立卫生研究院临床研究所）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）
A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care
University of Edinburgh（爱丁堡大学）；Health Data Research UK（英国健康数据研究中心）；Peking University（北京大学）；Peking University Third Hospital（北京大学第三医院）；Kaihong Digital（凯宏数字）
Imperial College London（伦敦帝国理工学院）；National University of Singapore（新加坡国立大学）；University of Copenhagen（哥本哈根大学）
The COVID-19 pandemic has caused over 6.4 million registered deaths to date and has had a profound impact on economic activity. Here, we study the interaction of transmission, mortality, and the economy during the SARS-CoV-2 pandemic from January 2020 to December 2022 across 25 European countries. We adopt a Bayesian Mixed Effects model with auto-regressive terms. We find that increases in disease transmission intensity decreases Gross domestic product (GDP) and increases daily excess deaths, with a longer lasting impact on excess deaths in comparison to GDP, which recovers more rapidly. Broad…
Texas A&M University（德克萨斯A&M大学）；Oklahoma State University（俄克拉荷马州立大学）
The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and mortality. In this paper, the factors that could affect the risk of COVID-19 infection and mortality were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that mean tempe…
Brac University（布拉克大学）；Islamic University of Technology（伊斯兰科技大学）；Khulna University of Engineering and Technology（库尔纳工程技术大学）；University of Toronto（多伦多大学）
COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus disease. Due to its contagious nature, the virus can easily infect an unprotected and exposed individual from mild to severe symptoms. The study of the virus effects on pregnant mothers and neonatal is now a concerning issue globally among civilians and public health workers considering how the virus will affect the mother and the neonates health. This paper aims to develop a pre…
Stanford University（斯坦福大学）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Fundacion Progreso y Salud（进步与健康基金会）；National Tsing Hua University（台湾清华大学）；Albert Einstein Medical Center（阿尔伯特·爱因斯坦医疗中心）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；National University Hospital（新加坡国立大学医院）；Hoboken University Medical Center–CarePoint Health（霍博肯大学医疗中心-CarePoint Health）；Hospital Universitario Monteprincipe（蒙特普林西比大学医院）；Grupo HM Hospitales（HM医院集团）；Seoul National University College of Medicine（首尔大学医学院）；Centro Nacional de Investigaciones Cardiovasculares（国家心血管研究中心）；Instituto de Salud Carlos III（卡洛斯三世健康研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology…
MIT Sloan School of Management（麻省理工学院斯隆管理学院）；Operations Research Center（运筹学中心）；MIT Center for Statistics（麻省理工学院统计中心）；Dana Farber Cancer Institute（达纳·法伯癌症研究所）；Harvard School of Public Health（哈佛大学公共卫生学院）
It is increasingly common to encounter prediction tasks in the biomedical sciences for which multiple datasets are available for model training. Common approaches such as pooling datasets and applying standard statistical learning methods can result in poor out-of-study prediction performance when datasets are heterogeneous. Theoretical and applied work has shown $\textit{multi-study ensembling}$ to be a viable alternative that leverages the variability across datasets in a manner that promotes model generalizability. Multi-study ensembling uses a two-stage $\textit{stacking}$ strategy which f…
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；University of California Santa Cruz（加州大学圣克鲁兹分校）
As the rapid spread of coronavirus disease (COVID-19) worldwide, chest X-ray radiography has also been used to detect COVID-19 infected pneumonia and assess its severity or monitor its prognosis in the hospitals due to its low cost, low radiation dose, and wide accessibility. However, how to more accurately and efficiently detect COVID-19 infected pneumonia and distinguish it from other community-acquired pneumonia remains a challenge. In order to address this challenge, we in this study develop and test a new computer-aided diagnosis (CAD) scheme. It includes several image pre-processing algo…
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）
Introduction: For COVID-19 patients accurate prediction of disease severity and mortality risk would greatly improve care delivery and resource allocation. There are many patient-related factors, such as pre-existing comorbidities that affect disease severity. Since rapid automated profiling of peripheral blood samples is widely available, we investigated how such data from the peripheral blood of COVID-19 patients might be used to predict clinical outcomes. Methods: We thus investigated such clinical datasets from COVID-19 patients with known outcomes by combining statistical comparison and c…
Stony Brook University（石溪大学）；Newark Beth Israel Medical Center（纽瓦克贝丝以色列医疗中心）；University Hospitals Cleveland Medical Center（大学医院克利夫兰医疗中心）
We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All …
JP Morgan Chase（摩根大通）；University of Cambridge（剑桥大学）
Chest Computational Tomography (CT) scans present low cost, speed and objectivity for COVID-19 diagnosis and deep learning methods have shown great promise in assisting the analysis and interpretation of these images. Most hospitals or countries can train their own models using in-house data, however empirical evidence shows that those models perform poorly when tested on new unseen cases, surfacing the need for coordinated global collaboration. Due to privacy regulations, medical data sharing between hospitals and nations is extremely difficult. We propose a GAN-augmented federated learning m…
Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
Healthcare data continues to flourish yet a relatively small portion, mostly structured, is being utilized effectively for predicting clinical outcomes. The rich subjective information available in unstructured clinical notes can possibly facilitate higher discrimination but tends to be under-utilized in mortality prediction. This work attempts to assess the gain in performance when multiple notes that have been minimally preprocessed are used as an input for prediction. A hierarchical architecture consisting of both convolutional and recurrent layers is used to concurrently model the differen…
Predicting Patient Recovery or Mortality Using Deep Neural Decision Tree and Forest
University of Tehran（德黑兰大学）；Tehran University of Medical Sciences（德黑兰医科大学）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Mashhad University of Medical Sciences（马什哈德医科大学）
Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data
Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学胃肠病与肝病研究所）；Faculty of Medicine, Isfahan University of Medical Sciences（伊斯法罕医科大学医学院）；Faculty of Business and Information Technology, Ontario Tech University（安大略理工大学商业与信息技术学院）；School of Medicine, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学医学院）；Digestive Disease Research Institute, Tehran University of Medical Sciences（德黑兰医科大学消化疾病研究所）；Department of Surgery, The Johns Hopkins University（约翰霍普金斯大学外科学系）；Student Research Committee, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学护理与助产学院学生研究委员会）；MPH department, Shiraz University of Medical Sciences（设拉子医科大学公共卫生硕士系）；Department of Emergency Medicine, School of Medicine, Safety Promotion and Injury Prevention Research Center, Imam Hossein Hospital, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学伊玛目侯赛因医院医学院急诊医学系安全促进与伤害预防研究中心）；Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center（西达赛奈医疗中心卡什胃肠病与肝病科）；Department of Medicine, Stanford University（斯坦福大学医学系）；Division of Data Driven and Digital Health (D3M), The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院查尔斯·布朗夫曼个性化医学研究所数据驱动与数字健康部）；Infectious Diseases and Tropical Medicine Research Center, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学传染病与热带医学研究中心）；Department of Infectious Diseases, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学洛格曼·哈基姆医院传染病科）；National Institute for Health and Care Research (NIHR), Nottingham Biomedical Research Centre, Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham（诺丁汉大学医学院国家健康与护理研究所诺丁汉生物医学研究中心听力科学、心理健康与临床神经科学）
