scholarly journals Machine learning-based prediction of COVID-19 diagnosis based on symptoms

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yazeed Zoabi ◽  
Shira Deri-Rozov ◽  
Noam Shomron
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Infected Individual ◽  
Learning Approach ◽  
Test Results ◽  
Risk Of Infection ◽  
Machine Learning Approach ◽  
Binary Features ◽  
Simple Features

AbstractEffective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

scholarly journals COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach

2020 ◽  
Author(s):  
Yazeed Zoabi ◽  
Noam Shomron
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Close Contact ◽  
Infected Individual ◽  
Learning Approach ◽  
Test Results ◽  
Machine Learning Approach ◽  
Simple Features ◽  
Limited Testing

AbstractEffective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed in hopes of assisting medical staff worldwide in triaging patients when allocating limited healthcare resources. We established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4,769 were confirmed COVID-19 cases) while the test set contained data from the following week (47,401 tested individuals of whom 3,624 were confirmed COVID-19 cases). Our model predicts COVID-19 test results with high accuracy using only 8 features: gender, whether the age is above 60, information about close contact with an infected individual, and 5 initial clinical symptoms. Overall, using nationwide data representing the general population, we developed a model that enables screening suspected COVID-19 patients according to simple features accessed by asking them basic questions. Our model can be used, among other considerations, to prioritize testing for COVID-19 when allocating limited testing resources.

Review Paper on Machine learning-based prediction of Corona Virus

2021 ◽  
Vol 9 (8) ◽  
pp. 2225-2229
Author(s):  
Miss. Aakansha P. Tiwari
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Symptoms ◽  
Contact Tracing ◽  
Test Results ◽  
Effective Contact ◽  
Machine Learning Approach ◽  
Binary Features ◽  
Simple Features ◽  
Medical Examiners

Abstract: Effective contact tracing of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and might mitigate the burden on healthcare system. Prediction models that combine several features to approximate the danger of infection are developed. These aim to help medical examiners worldwide in treatment of patients, especially within the context of limited healthcare resources. They established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to own COVID-19 coronavirus). Test set contained data from the upcoming week (47,401 tested individuals of whom 3624 were confirmed to own COVID-19 disease). Their model predicted COVID-19 test results with highest accuracy using only eight binary features: sex, age ≥60 years, known contact with infected patients, and also the appearance of 5 initial clinical symptoms appeared. Generally, supported the nationwide data publicly reported by the Israeli Ministry of Health, they developed a model that detects COVID-19 cases by simple features accessed by asking basic inquiries to the affected patient. Their framework may be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited and important. Keywords: Machine Learning, SARS-COV-2, COVID-19, Coronavirus.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach

2013 ◽  
Vol 25 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Mattia C. F. Prosperi ◽  
Danielle Belgrave ◽  
Iain Buchan ◽  
Angela Simpson ◽  
Adnan Custovic
Keyword(s):  
Machine Learning ◽  
Clinical Symptoms ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study

2020 ◽  
Vol 46 (4) ◽  
pp. 916-926 ◽  
Author(s):  
Jie Yang ◽  
Weidan Pu ◽  
Guowei Wu ◽  
Eric Chen ◽  
Edwin Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Negative Symptoms ◽  
Clinical Symptoms ◽  
Linear Regression Analysis ◽  
Symptom Burden ◽  
Learning Approach ◽  
Brain Connectome ◽  
Graph Properties ◽  
Machine Learning Approach

Abstract Background Working memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia. Methods We used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset. Results Small-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy. Conclusions We demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.

scholarly journals Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Moon-Jong Kim ◽  
Pil-Jong Kim ◽  
Hong-Gee Kim ◽  
Hong-Seop Kho
Keyword(s):  
Machine Learning ◽  
Treatment Outcomes ◽  
Clinical Data ◽  
Prediction Models ◽  
Burning Mouth Syndrome ◽  
Learning Approach ◽  
Initial Approach ◽  
Machine Learning Approach ◽  
Burning Mouth ◽  
Clonazepam Therapy

AbstractThe purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.

scholarly journals A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation

10.2196/23948 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e23948
Author(s):  
Yuanfang Chen ◽  
Liu Ouyang ◽  
Forrest S Bao ◽  
Qian Li ◽  
Lei Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Laboratory Test ◽  
Predictive Accuracy ◽  
Learning Approach ◽  
Test Results ◽  
Laboratory Test Results ◽  
Machine Learning Approach ◽  
Forest Models ◽  
Random Forest Models

Background Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

scholarly journals COVID-19 Pandemic Prediction for Hungary: A Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Gergo Pinter ◽  
Imre Felde ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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