Journal of medical Internet research
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J. Med. Internet Res. · Nov 2020
Associations Between COVID-19 Misinformation Exposure and Belief With COVID-19 Knowledge and Preventive Behaviors: Cross-Sectional Online Study.
Online misinformation proliferation during the COVID-19 pandemic has become a major public health concern. ⋯ COVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with fewer preventive behaviors. Given the potential of misinformation to undermine global efforts in COVID-19 disease control, up-to-date public health strategies are required to counter the proliferation of misinformation.
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J. Med. Internet Res. · Nov 2020
Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.
Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients' prognosis early and administer precise treatment are of great significance. ⋯ The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.
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J. Med. Internet Res. · Nov 2020
An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.
Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. ⋯ We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.
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J. Med. Internet Res. · Nov 2020
Impact of Remote Consultations on Antibiotic Prescribing in Primary Health Care: Systematic Review.
There has been growing international interest in performing remote consultations in primary care, particularly amidst the current COVID-19 pandemic. Despite this, the evidence surrounding the safety of remote consultations is inconclusive. The appropriateness of antibiotic prescribing in remote consultations is an important aspect of patient safety that needs to be addressed. ⋯ There is insufficient evidence to confidently conclude that remote consulting has a significant impact on antibiotic prescribing in primary care. However, studies indicating higher prescribing rates in remote consultations than in face-to-face consultations are a concern. Further, well-conducted studies are needed to inform safe and appropriate implementation of remote consulting to ensure that there is no unintended impact on antimicrobial resistance.
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J. Med. Internet Res. · Nov 2020
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.
COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. ⋯ We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.