The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.

/pdf/diagnosis-of-covid-19-using-chest-x-ray-images-and-disease-158mctu7.pdf

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

Abstract The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90–93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71–73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed ( https://alejandrocisterna.shinyapps.io/PROVIA ). 

/pdf/a-predictive-model-for-hospitalization-and-survival-to-covid-unolrx0l.pdf

A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

Aim To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment A bias assessment of AI models was done using PROBAST. Participants Patients tested positive for COVID-19. Results We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.

/pdf/artificial-intelligence-driven-prediction-of-covid-19-1phidw7y.pdf

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

Recent advancements in gene‐targeted therapies have highlighted the critical role data sharing plays in successful translational drug development for people with rare diseases. To scale these efforts, we need to systematize these sharing principles, creating opportunities for more rapid, efficient, and scalable drug discovery/testing including long‐term and transparent assessment of clinical safety and efficacy. A number of challenges will need to be addressed, including the logistical difficulties of studying rare diseases affecting individuals who may be scattered across the globe, scientific, technical, regulatory, and ethical complexities of data collection, and harmonization and integration across multiple platforms and contexts. The NCATS/NIH Gene‐Targeted Therapies: Early Diagnosis and Equitable Delivery meeting series held during June 2021 included data sharing models that address these issues and framed discussions of areas that require improvement. This article describes these discussions and provides a series of considerations for future data sharing.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ajmg.c.32028

Data sharing to advance gene‐targeted therapies in rare diseases

Comorbid asthma decreased the risk for COVID-19 mortality in the United Kingdom: Evidence based on a meta-analysis

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 deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

/pdf/machine-learning-approach-to-dynamic-risk-modeling-of-tvuv3k13ut.pdf

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study.

The COVID19 pandemic has resulted in over two million deaths globally There is an urgent need for robust, scalable monitoring tools supporting resource allocation and stratification of high-risk patients This research aims to develop and validate prediction models, using the UK Biobank to estimate COVID19 mortality risk in confirmed cases We developed a random forest classification model using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess risk of mortality with disease deterioration (AUC: 092) The design and feature selection of the framework lends itself to deployment in remote settings Possible applications include supporting individual-level risk profiling and monitoring disease progression across high volumes of patients with COVID19, especially in hospital-at-home settings
The COVID19 pandemic has precipitated over 100 million confirmed cases and 23 million deaths globally The impact of the pandemic has not been limited to healthcare systems: a ripple effect has resulted in wide-ranging economic and social disruption Interventions to reduce transmission, such as lockdowns, travel restrictions, and re-allocation of health resources, are critical to limiting the impact Although large-scale vaccination programmes have begun, many countries globally will not have widespread access to vaccines until 2023, meaning that non-pharmaceutical interventions are likely to remain indispensable national strategies for some time
COVID19 shows highly varied clinical presentation A significant proportion (17 to 45%) of cases are asymptomatic and require no specific care Conversely, reviews of severe complications have found that up to 32% of hospitalized COVID19 patients are admitted to ICU7 Between these two extremes, typical symptoms include fever, continuous cough, anosmia, and dyspnoea, which may range from requiring only self-management at home to inpatient care Understanding which individuals are most vulnerable to severe disease, and thereby in most need of resources, is critical to limit the impact of the virus
Decision-making at all levels requires an understanding of individuals risk of severe disease Various patient characteristics, comorbidities, and lifestyle factors have been linked to greater risk of death and/or severe illness following infection Furthermore, socioeconomic factors have also been linked as risk factors for COVID19 mortality Once patients are infected with SARS CoV 2, additional physiological parameters, such as symptoms and vital signs, can inform real-time prognostication13 Laboratory testing and imaging can also inform risk stratification for early, aggressive intervention, though this data is only accessible to hospital inpatients, who are likely to be already severely affected
Robust, predictive models for acquisition and prognosis of COVID 1916 18 and resource management have been developed to support risk stratification and population management at scale, offering important insights for organizational decision-making However, the individual is currently overlooked, and granular, patient-specific risk-scoring could potentially unify decision-making at all levels Existing individualized risk scores, however, often conflate risk of COVID19 acquisition with risk of mortality following infection, which can limit their utility in patient management
For prediction models to achieve impact at scale, assessment of risk factors should be inexpensive and accessible to the general population, ideally without the need for specialized testing or hospital visits Such risk prediction tools, enabling improved patient triage, could be used to further increase the efficiency of, and confidence in, hospital at home solutions, which have shown promise in reducing hospital burden throughout the pandemic Risk scores in these circumstances need to be dynamic and contemporaneous, ideally incorporating symptoms and vital sign data to maximise utility to clinical and research teams Therefore, the primary aim of this study is to develop and validate a population-based prediction model, using a large, rich dataset and a selective, clinically informed approach, which dynamically estimates the COVID19 mortality risk in confirmed diagnoses

https://www.medrxiv.org/content/medrxiv/early/2021/03/15/2021.02.08.21251343.full.pdf

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank cohort study

Diabetes affects over 400 million people and is among the leading causes of morbidity worldwide. Identification of high-risk individuals can support early diagnosis and prevention of disease development through lifestyle changes. However, the majority of existing risk scores require information about blood-based factors which are not obtainable outside of the clinic. Here, we aimed to develop an accessible solution that could be deployed digitally and at scale. We developed a predictive 10-year type 2 diabetes risk score using 301 features derived from 472,830 participants in the UK Biobank dataset while excluding any features which are not easily obtainable by a smartphone. Using a data-driven feature selection process, 19 features were included in the final reduced model. A Cox proportional hazards model slightly overperformed a DeepSurv model trained using the same features, achieving a concordance index of 0.818 (95% CI: 0.812-0.823), compared to 0.811 (95% CI: 0.806-0.815). The final model showed good calibration. This tool can be used for clinical screening of individuals at risk of developing type 2 diabetes and to foster patient empowerment by broadening their knowledge of the factors affecting their personal risk.

Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

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Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank cohort study

Development of a dynamic type 2 diabetes risk prediction tool: a UK Biobank study.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study