Schmidtmcdermott9836

Developing or independently evaluating algorithms in biomedical research is difficult due to restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of these barriers to data sharing. For example, researchers can access data in high-performance, secure, and auditable cloud computing environments without the need for copying or download. An alternative path to accessing datasets requiring additional protections is the "Model to Data" (M2D) approach. In M2D researchers submit algorithms to run on secure datasets that remain hidden. M2D is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. M2D has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that M2D can make a valuable addition to our data sharing arsenal, with two caveats. First, M2D should only be adopted where necessary to supplement rather than replace existing data sharing approaches, given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, while M2D does reduce concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt M2D approaches without guidance on how to do so responsibly. To address this gap, we explore how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in an M2D context.Background The coronavirus disease (COVID-19) has become an urgent and serious global public health crisis. Community engagement is the first line of defense in the fight against infectious diseases, and general practitioners (GPs) play an important role in it. GPs are facing unique challenges from disasters and pandemics in delivering health care. However, there is still no suitable mobile management system that can help GPs collect data, dynamically assess risks, and effectively triage or follow-up with patients with COVID-19. Objective The aim of this study is to design, develop, and deploy a mobile-based decision support system for COVID-19 (DDC19) to assist GPs in collecting data, assessing risk, triaging, managing, and following up with patients during the COVID-19 outbreak. Methods Based on the actual scenarios and the process of patients using health care, we analyzed the key issues that need to be solved and designed the main business flowchart of DDC19. We then constructed a COVID-19 dynamic risk st clinical symptoms, and contact history; (2243, 35) when the results of blood tests were added; and (2243, 36) after obtaining the computed tomography imaging results of the patient. The average value of the three classification results of the macro-area under the curve were all above 0.71 in each scenario. Conclusions DCC19 is a mobile decision support system designed and developed to assist GPs in providing dynamic risk assessments for patients with suspected COVID-19 during the outbreak, and the model had a good ability to predict risk levels in any scenario it covered.Background Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19-related health information. Objective The aim of this paper is to analyze web search data volumes related to COVID-19 in China. Methods We conducted an infodemiological study to analyze web search data volumes related to COVID-19. this website Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020). Results The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, al panic, and prevention and control policies in response to COVID-19.Background Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at ~40% for home-based exercise prescriptions, implementing a remote-sensing system would help patients and clinicians understand treatment progress and increase compliance. Inclusion of end users in the development of mobile applications for remote sensing systems can ensure that they are both user-friendly and facilitate compliance. With advancements in natural-language processing (NLP) there is potential for these methods to be used with data collected through the user-centered design process. Objective The objective of our study was to develop a mobile application for a novel device through a user-centered design process with both older adults and clinicians while exploring if data collected through this process can be used in natural language processing and sentiment analysis methods. Methods Through a user-centered design process,logy for older adults.Background In 2017, 9% of the population of adults with diabetes could receive digital care. By 2045, digital care will increase by 48%. One Drop's (OD) digital care solution includes an evidence-based mobile app, a Bluetooth-connected glucometer, and in-app coaching from Certified Diabetes Educators. Using OD is associated with a 3-mo. -22.2 mg/dL (-.80% eA1c) among people with type 1 diabetes (T1D) and eA1c ≥ 7.5%. The added value of integrated activity trackers is unknown. Objective We conducted a pragmatic, remotely administered, randomized control trial to evaluate One Drop with a new-to-market activity tracker on the A1c of adults with T1D. Methods Social media advertisements and online newsletters recruited adults (≥ 18 years old) diagnosed (≥ 1 year) with T1D, naïve to OD's full solution and the activity tracker with lab A1c ≥ 7%. Participants (N = 99) were randomized to get OD plus activity tracker at study start or OD at start and an activity tracker after 3 mos. Multiple imputation, performed separately by group, corrected for missing data.