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Public Health Public Health

A Sentiment Analysis Approach to Predict an Individual's Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia.

In International journal of environmental research and public health ; h5-index 73.0

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual's awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency-Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

Aljameel Sumayh S, Alabbad Dina A, Alzahrani Norah A, Alqarni Shouq M, Alamoudi Fatimah A, Babili Lana M, Aljaafary Somiah K, Alshamrani Fatima M


Arabic sentiment analysis, K-nearest neighbor, N-gram, Twitter, machine learning, natural language processing, naïve bayes, support vector machine

General General

Designing a Randomized Trial with an Age Simulation Suit-Representing People with Health Impairments.

In Healthcare (Basel, Switzerland)

Due to demographic change, there is an increasing demand for professional care services, whereby this demand cannot be met by available caregivers. To enable adequate care by relieving informal and formal care, the independence of people with chronic diseases has to be preserved for as long as possible. Assistance approaches can be used that support promoting physical activity, which is a main predictor of independence. One challenge is to design and test such approaches without affecting the people in focus. In this paper, we propose a design for a randomized trial to enable the use of an age simulation suit to generate reference data of people with health impairments with young and healthy participants. Therefore, we focus on situations of increased physical activity.

Timm Ingo J, Spaderna Heike, Rodermund Stephanie C, Lohr Christian, Buettner Ricardo, Berndt Jan Ole


experimental design, fitness tracker, physical activity, questionnaires

General General

Probabilistic Predictions with Federated Learning.

In Entropy (Basel, Switzerland)

Probabilistic predictions with machine learning are important in many applications. These are commonly done with Bayesian learning algorithms. However, Bayesian learning methods are computationally expensive in comparison with non-Bayesian methods. Furthermore, the data used to train these algorithms are often distributed over a large group of end devices. Federated learning can be applied in this setting in a communication-efficient and privacy-preserving manner but does not include predictive uncertainty. To represent predictive uncertainty in federated learning, our suggestion is to introduce uncertainty in the aggregation step of the algorithm by treating the set of local weights as a posterior distribution for the weights of the global model. We compare our approach to state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms. By applying proper scoring rules to evaluate the predictive distributions, we show that our approach can achieve similar performance as the benchmark would achieve in a non-distributed setting.

Thorgeirsson Adam Thor, Gauterin Frank


Bayesian deep learning, federated learning, predictive uncertainty, probabilistic machine learning

Radiology Radiology

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.

In Diagnostics (Basel, Switzerland)

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Guiot Julien, Vaidyanathan Akshayaa, Deprez Louis, Zerka Fadila, Danthine Denis, Frix Anne-Noëlle, Thys Marie, Henket Monique, Canivet Gregory, Mathieu Stephane, Eftaxia Evanthia, Lambin Philippe, Tsoutzidis Nathan, Miraglio Benjamin, Walsh Sean, Moutschen Michel, Louis Renaud, Meunier Paul, Vos Wim, Leijenaar Ralph T H, Lovinfosse Pierre


COVID-19, artificial intelligence, computed tomography, machine learning, radiomics

Radiology Radiology

Factors associated with worsening oxygenation in patient with non-severe COVID-19 pneumonia.

In Tuberculosis and respiratory diseases

Background : This study aimed to determine parameters for worsening oxygenation in non-severe COVID-19 pneumonia.

Methods : This retrospective cohort study included confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as those with SpO2 ≤ 94%, or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening group who were without any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest CT were calculated using artificial intelligence (AI) and measured visually by a radiologist.

Results : We included 136 patients with 32 (23.5%) in the worsening oxygenation group, of whom two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups, however, univariate logistic regression analysis revealed that a variety of parameters at admission were associated with an increased risk of a desaturation event. A subset of patients were studied to eliminate potential bias, that ferritin ≥ 280 μg/L (p=0.029), LDH ≥ 240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52).

Conclusion : Our study presents initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation at admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

Hahm Cho Rom, Lee Young Kyung, Oh Dong Hyun, Ahn Mi Young, Choi Jae-Phil, Kang Na Ree, Oh Jungkyun, Choi Hanzo, Kim Suhyun


COVID-19, Computed tomography, Oxygenation, Pneumonia, artificial intelligence

Public Health Public Health

Artificial Intelligence Model of Drive-Through Vaccination Simulation.

In International journal of environmental research and public health ; h5-index 73.0

Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

Asgary Ali, Valtchev Svetozar Zarko, Chen Michael, Najafabadi Mahdi M, Wu Jianhong


COVID-19 pandemic, artificial intelligence, discrete event simulation, drive-through, mass vaccination