COVID-19 diagnosis by routine blood tests using machine learning

• URL: http://arxiv.org/abs/2006.03476v1
• Date: Thu, 4 Jun 2020 14:57:17 GMT
• Title: COVID-19 diagnosis by routine blood tests using machine learning
• Authors: Matja\v{z} Kukar, Gregor Gun\v{c}ar, Toma\v{z} Vovko, Simon Podnar, Peter \v{C}ernel\v{c}, Miran Brvar, Mateja Zalaznik, Mateja Notar, Sa\v{s}o Mo\v{s}kon, Marko Notar
• Abstract summary: We constructed a machine learning predictive model for COVID-19 diagnosis. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis.

Related papers

• Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application [0.0]
  Histogram-based Gradient Boosting (HGB) was used to detect the disease with 100% accuracy, learning time 6.39 sec. We propose to use these 11 traits and their combinations as important biomarkers for ML sensors in diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
  arXiv  Detail & Related papers  (2022-09-08T01:35:45Z)
• A New Feature Selection Method for LogNNet and its Application for Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values [0.0]
  The aim of this study is to determine the most effective routine-blood-values in the diagnosis/prognosis of COVID-19 using new feature selection method for LogNNet reservoir neural network. LogNNet model demonstrated a very high disease diagnosis/prognosis of COVID-19 performance without knowing about the symptoms or history of the patients.
  arXiv  Detail & Related papers  (2022-05-20T05:47:29Z)
• CovXR: Automated Detection of COVID-19 Pneumonia in Chest X-Rays through Machine Learning [0.0]
  COVID-19 is the highly contagious illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Patients who test positive for COVID-19 demonstrate diffuse alveolar damage in 87% of cases. CovXR is a machine learning model designed to detect COVID-19 pneumonia in chest X-rays.
  arXiv  Detail & Related papers  (2021-10-12T23:21:13Z)
• Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 [6.941255691176647]
  We propose a novel residual network to automatically identify COVID-19 from other common pneumonia and normal people using CT images. Our method can differentiate COVID-19 from the other two classes with 94.7% accuracy, 93.73% sensitivity, 98.28% specificity, 95.26% F1-score, and an area under the receiver operating characteristic curve (AUC) of 0.99.
  arXiv  Detail & Related papers  (2021-05-14T11:59:47Z)
• Effect of Different Batch Size Parameters on Predicting of COVID19 Cases [0.0]
  The effect of different batch size on their performance in detecting COVID19 and other classes was investigated. The highest COVID19 detection was 95.17% for BH = 3, while the overall accuracy value was 97.97% with BH = 20.
  arXiv  Detail & Related papers  (2020-12-10T09:25:05Z)
• Classification supporting COVID-19 diagnostics based on patient survey data [82.41449972618423]
  logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19 were generated. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. This data set consists of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
  arXiv  Detail & Related papers  (2020-11-24T17:44:01Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia [46.521323145636906]
  We develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT) In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%.
  arXiv  Detail & Related papers  (2020-05-06T09:56:51Z)
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation [95.57532063232198]
  coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries. To control the infection, identifying and separating the infected people is the most crucial step. This paper develops a novel Joint Classification and (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis.
  arXiv  Detail & Related papers  (2020-04-15T12:30:40Z)
• Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images [54.919022945740515]
  The aim of this study is to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images. A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features. Several quantitative features, which have the potential to reflect the severity of COVID-19, were revealed.
  arXiv  Detail & Related papers  (2020-03-26T15:49:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.