were addressed by the physicians during the pandemic.

There was a high acceptance of telemedicine services by the patients, which was evident by a high show rate during the COVID-19 pandemic in Detroit. With limited staffing, restricted outpatient work hours, a shortage of providers, and increased outpatient needs, telemedicine was successfully implemented in our practice.

There was a high acceptance of telemedicine services by the patients, which was evident by a high show rate during the COVID-19 pandemic in Detroit. With limited staffing, restricted outpatient work hours, a shortage of providers, and increased outpatient needs, telemedicine was successfully implemented in our practice.

Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability.

We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days.

Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator.

The LASSO

model outperformed the LASSO

model at 3 hospitals, and the MLP

model performed better than the MLP

model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSO

model outperformed the LASSO

model at all hospitals, and the MLP

model outperformed the MLP

model at 2 hospitals.

The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

The COVID-19 pandemic is a severe global health crisis. Wearing a mask is a straightforward action that can be taken, but shortage of stock and equity of allocation were important issues in Taiwan. Furthermore, increased anxiety leading to the stockpiling of masks has been common during the pandemic.

We aim to summarize the name-based mask rationing plan implemented in Taiwan and explore the public’s perceived anxiety about mask shortages.

The government of Taiwan took action to control the supply and allocation of face masks. We summarize the timeline and important components of the mask rationing plan. A survey that aimed to investigate the overall response to the mask rationing plan was answered by 44 participants.

The mask rationing plan was implemented in late January 2020. ORY-1001 datasheet Daily production capacity was increased from 2 million masks to 16 million masks in April 2020. People could buy 9 masks in 14 days by verification via their National Health Insurance card. Digital face mask availability maps pandemic.The COVID-19 pandemic has caused substantial global disturbance by affecting more than 42 million people (as of the end of October 2020). Since there is no medication or vaccine available, the only way to combat it is to minimize transmission. Digital contact tracing is an effective technique that can be utilized for this purpose, as it eliminates the manual contact tracing process and could help in identifying and isolating affected people. However, users are reluctant to share their location and contact details due to concerns related to the privacy and security of their personal information, which affects its implementation and extensive adoption. Blockchain technology has been applied in various domains and has been proven to be an effective approach for handling data transactions securely, which makes it an ideal choice for digital contact tracing apps. The properties of blockchain such as time stamping and immutability of data may facilitate the retrieval of accurate information on the trail of the virus in a transparent manner, while data encryption assures the integrity of the information being provided. Furthermore, the anonymity of the user’s identity alleviates some of the risks related to privacy and confidentiality concerns. In this paper, we provide readers with a detailed discussion on the digital contact tracing mechanism and outline the apps developed so far to combat the COVID-19 pandemic. Moreover, we present the possible risks, issues, and challenges associated with the available contact tracing apps and analyze how the adoption of a blockchain-based decentralized network for handling the app could provide users with privacy-preserving contact tracing without compromising performance and efficiency.

The COVID-19 pandemic triggered countermeasures like #StayAtHome initiatives, which have changed the whole world. Despite the success of such initiatives in limiting the spread of COVID-19 to #FlattenTheCurve, physicians are now confronted with the adverse effects of the current restrictive pandemic management strategies and social distancing measures.

We aim to draw attention to the particular importance and magnitude of what may be the adverse effects of COVID-19-related policies.

We herein report a case of an otherwise healthy 84-year-old woman with deep vein thrombosis (DVT) due to COVID-19-related directives. #StayAtHome policies and consequential social isolation have diminished our patient’s social life and reduced her healthy movement behaviors. The patient spent long hours in a seated position while focusing on the intensive flow of media information regarding the pandemic.

Reduced mobility due to preventive social isolation during the COVID-19 pandemic was the only identified cause of the DVT.

While evaluating the effect of the COVID-19 pandemic and governmentally implemented containment measures, including social isolation and mobility reduction, adverse events should be considered. Digital approaches might play a crucial role in supporting public health.

While evaluating the effect of the COVID-19 pandemic and governmentally implemented containment measures, including social isolation and mobility reduction, adverse events should be considered. Digital approaches might play a crucial role in supporting public health.