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）
Kai Zhang, Siddharth Karanth, Bela Patel, Robert Murphy, Xiaoqian Jiang
We propose a robust in-time predictor for in-hospital COVID-19 patient's probability of requiring mechanical ventilation. A challenge in the risk prediction for COVID-19 patients lies in the great variability and irregular sampling of patient's vitals and labs observed in the clinical setting. Existing methods have strong limitations in handling time-dependent features' complex dynamics, either oversimplifying temporal data with summary statistics that lose information or over-engineering features that lead to less robust outcomes. We propose a novel in-time risk trajectory predictive model to handle the irregular sampling rate in the data, which follows the dynamics of risk of performing mechanical ventilation for individual patients. The model incorporates the Multi-task Gaussian Process using observed values to learn the posterior joint multi-variant conditional probability and infer the missing values on a unified time grid. The temporal imputed data is fed into a multi-objective self-attention network for the prediction task. A novel positional encoding layer is proposed and added to the network for producing in-time predictions. The positional layer outputs a risk score at each user-defined time point during the entire hospital stay of an inpatient. We frame the prediction task into a multi-objective learning framework, and the risk scores at all time points are optimized altogether, which adds robustness and consistency to the risk score trajectory prediction. Our experimental evaluation on a large database with nationwide in-hospital patients with COVID-19 also demonstrates that it improved the state-of-the-art performance in terms of AUC (Area Under the receiver operating characteristic Curve) and AUPRC (Area Under the Precision-Recall Curve) performance metrics, especially at early times after hospital admission.
Background: The COVID-19 pandemic has had a profound impact on health, everyday life and economics around the world. An important complication that can arise in connection with a COVID-19 infection is acute kidney injury. A recent observational cohort study of COVID-19 patients treated at multiple sites of a tertiary care center in Berlin, Germany identified risk factors for the development of (severe) acute kidney injury. Since inferring results from a single study can be tricky, we validate these findings and potentially adjust results by including external information from other studies on …
Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously adjust ventilator settings for each patient, a challenging and time consuming task. Hence, it would be beneficial to develop an automated decision support tool to optimize ventilation treatment. We present DeepVent, a Conservative Q-Learning (CQL) based offline Deep Reinforcement Learning (DRL) agent that learns to predict the optimal ventilator parameters for a patient to promote 90 day survival. We design a clinically relevant intermediate reward that e…
Universidade de Lisboa（里斯本大学）；Instituto Superior Técnico（高等技术学院）；Instituto de Medicina Molecular（分子医学研究所）；Hospital de Santa Maria, CHLN（圣玛丽亚医院，CHLN）
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted ventilation in ALS patients. However, the clinical translation of such models is limited by the lack of insight 1) on the risk of error for predictions at patient-level, and 2) on the most adequate time to administer the non-invasive ventilation. To address these issues, we combine Conformal Prediction (a machine learning fr…
University of Michigan（密歇根大学）；Fudan University（复旦大学）；Shenzhen University（深圳大学）
Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks can be recovered or not depends, to a large extent, on how early they will be treated appropriately before irreversible consequences are caused to the patients by the virus. In this research, we reported an explainable, intuitive, and accurate machine learning model based on logistic regression to predict the fatality rate of COVID-19 patients using only three important blood biomarkers, including lactic dehydrogenase, lymphocyte (%) and high-sensitivity C-reactive protein, and their interactions. We found that when …
Khulna University of Engineering & Technology（库尔纳工程技术大学）
A ventilator simulation system can make mechanical ventilation easier and more effective. As a result, predicting a patient's ventilator pressure is essential when designing a simulation ventilator. We suggested a hybrid deep learning-based approach to forecast required ventilator pressure for patients. This system is made up of Bi-LSTM and Bi-GRU networks. The SELU activation function was used in our proposed model. MAE and MSE were used to examine the accuracy of the proposed model so that our proposed methodology can be applied to real-world problems. The model performed well against test d…
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…
Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challenges remain. We frame these as a control problem: ventilators must let air in and out of the patient's lungs according to a prescribed trajectory of airway pressure. Industry-standard controllers, based on the PID method, are neither optimal nor robust. Our data-driven approach learns to control an invasive ventilator by training on a simulator itself trained on data collected from the ventilator. This method outperf…
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）
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 …
Ensuring Safety in Automated Mechanical Ventilation through Offline Reinforcement Learning and Digital Twin Verification
School of Engineering, University of Warwick, Coventry, UK（沃里克大学工程学院，英国考文垂）；School of Computer Science, Chongqing University, China（重庆大学计算机科学学院，中国）；School of Computer Science and Technology, Hainan University, China（海南大学计算机科学与技术学院，中国）；Institute for Computational Biomedicine, RWTH Aachen University, Germany（鲁姆哈根工业大学计算生物医学研究所，德国）
Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized or even to receive intensive medical care (e.g., invasive mechanical ventilation or cardiovascular support) to recover from the illnesses. Therefore, it is critical to predict the severe health risk that COVID-19 infection poses to children to provide precise and timely medical care for vulnerable pediatric COVID-19 patients. However, predicting the severe health risk for COVID-19 patients including children remains a significan…
Massachusetts General Hospital（麻省总医院）；Mass General Brigham（麻省百瀚医疗系统）；Harvard Medical School（哈佛医学院）
We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting the patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients' past medical records (before their COVID-19 infection). MLHO's architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning a…
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician. Hand-tuned PID controllers and similar variants have comprised the industry standard for decades, yet can behave poorly by over- or under-shooting their target or oscillating rapidly. We consider a data-driven machine learning approach: First, we train a simulator based on data we collect from an artificial lung. Then, we train deep neural network co…
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…
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）
DarwinAI Corp.（DarwinAI公司）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）
The COVID-19 pandemic continues to have a devastating global impact, and has placed a tremendous burden on struggling healthcare systems around the world. Given the limited resources, accurate patient triaging and care planning is critical in the fight against COVID-19, and one crucial task within care planning is determining if a patient should be admitted to a hospital's intensive care unit (ICU). Motivated by the need for transparent and trustworthy ICU admission clinical decision support, we introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient cl…
Istanbul Medipol University（伊斯坦布尔梅迪波尔大学）；SRTM University Nanded（SRTM大学楠德分校）
Using machine learning algorithms for the rapid diagnosis and detection of the COVID-19 pandemic and isolating the patients from crowded environments are very important to controlling the epidemic. This study aims to develop a point-of-care testing (POCT) system that can detect COVID-19 by detecting volatile organic compounds (VOCs) in a patient's exhaled breath using the Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and March 2021. 84 cases out of 142 resulted in negatives, and 58 cases res…
Università degli Studi di Brescia（布雷西亚大学）；ASST Spedali Civili di Brescia（布雷西亚市民医院）
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…
Indian Institute of Technology Delhi（德里印度理工学院）；University of Bath（巴斯大学）；Indian Institute of Technology Kanpur（坎普尔印度理工学院）；All India Institute of Medical Sciences Delhi（德里全印度医学科学研究所）
The exponential increase in COVID-19 patients is overwhelming healthcare systems across the world. With limited testing kits, it is impossible for every patient with respiratory illness to be tested using conventional techniques (RT-PCR). The tests also have long turn-around time, and limited sensitivity. Detecting possible COVID-19 infections on Chest X-Ray may help quarantine high risk patients while test results are awaited. X-Ray machines are already available in most healthcare systems, and with most modern X-Ray systems already digitized, there is no transportation time involved for the …
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…
University of California Irvine（加州大学欧文分校）；University of Turku（图尔库大学）；University of Miami（迈阿密大学）
The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (7.9 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be…
There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID-19 patient shielding -- identifying and protecting patients who are clinically extremely vulnerable from coronavirus. This study focuses on techniques used for the multi-label classification of medical text. Using the information published by the United Kingdom NHS and the World Health Organisation, we present a novel approach to predicting COVID-19 patient shielding as a mult…
Machine Learning-driven Analysis of Gastrointestinal Symptoms in Post-COVID-19 Patients
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）；College of Medicine, Kufa University（库法大学医学院）；Imperial College（帝国理工学院）；Al-Sadder Teaching Hospital（萨德尔教学医院）
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…
University of Minnesota, Twin Cities（明尼苏达大学双城分校）；Allina Health（阿利纳医疗）
Leading up to August 2020, COVID-19 has spread to almost every country in the world, causing millions of infected and hundreds of thousands of deaths. In this paper, we first verify the assumption that clinical variables could have time-varying effects on COVID-19 outcomes. Then, we develop a temporal stratification approach to make daily predictions on patients' outcome at the end of hospital stay. Training data is segmented by the remaining length of stay, which is a proxy for the patient's overall condition. Based on this, a sequence of predictive models are built, one for each time segment…
During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and clinical workflow. In view of this need, we present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from …
Automated Detection of Persistent Inflammatory Biomarkers in Post-COVID-19 Patients Using Machine Learning Techniques
Era University（埃拉大学）；Era's Lucknow Medical College and Hospital（埃拉克克瑙医学院及医院）；College of Medicine, Kufa University（库法大学医学院）；University of Al-Qadisiyah（卡迪西亚大学）；Liverpool John Moors University（利物浦约翰摩尔大学）
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…
University at Buffalo – The State University of New York（纽约州立大学布法罗分校）；American Medical Association（美国医学会）
This study evaluated post traumatic stress disorder (PTSD) among frontline US physicians (treating COVID-19 patients) in comparison with second-line physicians (not treating COVID-19 patients), and identified the significance and patterns of factors associated with higher PTSD risk. A cross-sectional, web-based survey was deployed during August and September, 2020, to practicing physicians in the 18 states with the largest COVID-19 cases. Among 1,478 responding physicians, 1,017 completed the PTSD Checklist (PCL-5). First, the PCL-5 was used to compare symptom endorsement between the two physi…
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…
COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data
Stony Brook University（石溪大学）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（达尔文人工智能公司）；McGill University（麦吉尔大学）
Northeastern University（东北大学）；New York University School of Medicine（纽约大学医学院）
Patients with severe Coronavirus disease 19 (COVID-19) typically require supplemental oxygen as an essential treatment. We developed a machine learning algorithm, based on a deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critical ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice. Basically, we modeled the oxygen flow trajectory of COVID-19 patients and their health outcomes as a Markov decision process. Based on individual …
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（达尔文人工智能公司）
The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease, there is a pressing need for rapid and effective screening tools to identify patients infected with COVID-19, and to this end CT imaging has been proposed as one of the key screening methods which may be used as a complement to RT-PCR testing, particularly in situations where patients undergo routine CT scans for non-COVID-19 related reasons, patients with worsening respiratory status or developing complications tha…
Although mechanical ventilation is a lifesaving intervention in the ICU, it has harmful side-effects, such as barotrauma and volutrauma. These harms can occur due to asynchronies. Asynchronies are defined as a mismatch between the ventilator timing and patient respiratory effort. Automatic detection of these asynchronies, and subsequent feedback, would improve lung ventilation and reduce the probability of lung damage. Neural networks to detect asynchronies provide a promising new approach but require large annotated data sets, which are difficult to obtain and require complex monitoring of in…
In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest Xray (CXR) images and generalized to COVID-19 pneumonia. The segmentations were utilized to calculate a "Pneumonia Ratio" which indicates the disease severity. The measurement of disease severity enables to build a disease extent profile over time for hospitalized patients. To validate the model relevance to the patient monitoring task, we devel…
MGH & BWH Center for Clinical Data Science（麻省总医院与布列根和妇女医院临床数据科学中心）；Massachusetts General Hospital（麻省总医院）；Harvard University（哈佛大学）；NVIDIA（英伟达）
COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hospital resources management and cross-infection control. We trained a deep feature fusion model to predict patient outcomes, where the model inputs were EHR data including demographic information, co-morbidities, vital signs and laboratory measurements, plus patient's CXR images. The model output was patient outcomes defined as the most insensitive oxygen therapy required. For patients without CXR images, we employed R…
Predicting Long-term Renal Impairment in Post-COVID-19 Patients with Machine Learning Algorithms
University of Al-Qadisiyah（卡迪西亚大学）；Liverpool John Moors University（利物浦约翰摩尔大学）；University Hospital of Wales（威尔士大学医院）；Kufa University（库法大学）；Imperial College（帝国理工学院）
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…
Long-term Neurological Sequelae in Post-COVID-19 Patients: A Machine Learning Approach to Predict Outcomes
University Hospital of Wales（威尔士大学医院）；University of Al-Qadisiyah（卡迪西亚大学）；College of Medicine, Kufa University（库法大学医学院）；Imperial College（帝国理工学院）；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）
Unidad Mixta de Imagen Biomédica FISABIO-CIPF（FISABIO-CIPF生物医学成像联合单元）；Universidad de Alicante（阿利坎特大学）；Medbravo；Hospital San Juan de Alicante（阿利坎特圣胡安医院）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）；Sierra Research SL（Sierra Research有限公司）；Bioinformatics & Biostatistics Unit, Principe Felipe Research Center（费利佩王子研究中心生物信息学与生物统计学单元）
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerab…
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…
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…
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）
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…
In this work, we modify and apply self-supervision techniques to the domain of medical health insurance claims. We model patients' healthcare claims history analogous to free-text narratives, and introduce pre-trained `prior knowledge', later utilized for patient outcome predictions on a challenging task: predicting Covid-19 hospitalization, given a patient's pre-Covid-19 insurance claims history. Results suggest that pre-training on insurance claims not only produces better prediction performance, but, more importantly, improves the model's `clinical trustworthiness' and model stability/relia…
University of Wisconsin（威斯康星大学）；Henry Ford Health（亨利福特医疗集团）
Purpose: To answer the long-standing question of whether a model trained from a single clinical site can be generalized to external sites. Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264 COVID-19-positive patients and 4,802 COVID-19-negative patients were collected from a single site for AI model development. The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277 COVID-19-positive patients). The area under the receiver operating characteristic curve…
National Institute of Technology Karnataka - Surathkal（卡纳塔克国家理工学院苏拉特卡尔分校）
In this paper, we try to investigate the presence of cues about the COVID-19 disease in the speech data. We use an approach that is similar to speaker recognition. Each sentence is represented as super vectors of short term Mel filter bank features for each phoneme. These features are used to learn a two-class classifier to separate the COVID-19 speech from normal. Experiments on a small dataset collected from YouTube videos show that an SVM classifier on this dataset is able to achieve an accuracy of 88.6% and an F1-Score of 92.7%. Further investigation reveals that some phone classes, such a…
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）
The global COVID-19 pandemic has caused more than six million deaths worldwide. Medicalized hotels were established in Taiwan as quarantine facilities for COVID-19 patients with no or mild symptoms. Due to limited medical care available at these hotels, it is of paramount importance to identify patients at risk of clinical deterioration. This study aimed to develop and evaluate a graph-based deep learning approach for progressive hospital transfer risk prediction in a medicalized hotel setting. Vital sign measurements were obtained for 632 patients and daily patient similarity graphs were cons…
