Journal Article•
predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019
TL;DR: Clinical predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19, and therefore help inform care and prioritise resources.
Abstract: BACKGROUND: Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Due to the rapid human-to-human transmission of SARS-CoV-2, many healthcare systems are at risk of exceeding their healthcare capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds and mechanical ventilators Predictive algorithms could potentially ease the strain on healthcare systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalised or admitted to the ICU OBJECTIVE: To develop, study and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care METHODS: Using a systematic approach to model development and optimisation, we train and compare various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting To evaluate the developed models, we perform a retrospective evaluation on demographic, clinical and blood analysis data from a cohort of 5644 patients In addition, we determine which clinical features are predictive to what degree for each of the aforementioned clinical tasks using causal explanations RESULTS: Our experimental results indicate that our predictive models identify (i) patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% confidence interval [CI]: 67%, 81%) and a specificity of 49% (95% CI: 46%, 51%), (ii) SARS-CoV-2 positive patients that require hospitalisation with 0 92 area under the receiver operator characteristic curve [AUC] (95% CI: 0 81, 0 98), and (iii) SARS-CoV-2 positive patients that require critical care with 0 98 AUC (95% CI: 0 95, 1 00) CONCLUSIONS: Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19, and therefore help inform care and prioritise resources
Citations
More filters
TL;DR: A review of recent reports on ML algorithms used in relation to COVID-19 can be found in this paper, where the authors focus on the potential of ML for two main applications: diagnosis of COVID19 and prediction of mortality risk and severity, using readily available clinical and laboratory data.
93 citations
TL;DR: The proposed ERLX is robust and can be deployed for reliable early and rapid screening of COVID-19 patients and revealed better performance when compared against existing state-of-the-art studies for the same set of features employed by them.
73 citations
TL;DR: In this article, a metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images is presented, which comprises different preprocessing, feature extraction, and classification processes.
Abstract: In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.
13 citations
TL;DR: In this article, the authors used machine learning algorithms to predict the severity level of the Covid-19 pandemic in two stages; in stage one, no preprocessing method has applied while in stage two preprocessing has emphasized for achieving better prediction results.
Abstract: An increase in the number of patients and death rates make Covid-19 a serious pandemic situation. This problem has effects on health security, economical security, social life, and many others. The long and unreliable diagnosis process of the Covid-19 makes the disease spread even faster. Therefore, fast and efficient diagnosis is significant for dealing with this pandemic. Computer-aided medical diagnosis systems are very common applications and due to the importance of the problem, providing accurate predictions is required. In this study, blood samples of patients from Einstein Hospital in Brazil has collected and used for prediction on the severity level of Covid-19 with machine learning algorithms. The study was constructed in two stages; in stage-one, no preprocessing method has applied while in stage-two preprocessing has emphasized for achieving better prediction results. At the end of the study, 0.98 accuracy was obtained with the tuned Random Forest algorithm and several preprocessing methods.
10 citations
Posted Content•
TL;DR: This work proposes an algorithm that works for purely observational data, while also offering theoretical guarantees, including the case of partially nonlinear relationships, and can apply even to large graphs, demonstrating significant improvements compared to established approaches.
Abstract: The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent work in the field of causal discovery exploits invariance properties of models across different experimental conditions for detecting direct causal links. However, these approaches generally do not scale well with the number of explanatory variables, are difficult to extend to nonlinear relationships, and require data across different experiments. Inspired by {\em Debiased} machine learning methods, we study a one-vs.-the-rest feature selection approach to discover the direct causal parent of the response. We propose an algorithm that works for purely observational data, while also offering theoretical guarantees, including the case of partially nonlinear relationships. Requiring only one estimation for each variable, we can apply our approach even to large graphs, demonstrating significant improvements compared to established approaches.
7 citations
Cites methods or result from "predCOVID-19: A Systematic Study of..."
...D.2 Results The results obtained by leveraging CORTH Features is suprisingly consistent with (Schwab et al., 2020) which demonstrates the ability of this method in feature selection....
[...]
...For an existing and extensive analysis of the dataset with predictive methods, we refer to Schwab et al. (2020)....
[...]
...It is encouraging that some of these variables are consistent with other studies Schwab et al. (2020)....
[...]
...D.1 Preprocessing The preprocessing stage for this dataset is the same as (Schwab et al., 2020) except that, for each target variable upsampling is used to resolve data imbalance....
[...]