Martinpilgaard5871

We conducted a systematic review of studies evaluating the cost-effectiveness (CE) of interventions to prevent type 2 diabetes (T2D) among high-risk individuals and whole populations.

Interventions targeting high-risk individuals are those that identify people at high risk of developing T2D and then treat them with either lifestyle or metformin interventions. Population-based prevention strategies are those that focus on the whole population regardless of the level of risk, creating public health impact through policy implementation, campaigns, and other environmental strategies. We systematically searched seven electronic databases for studies published in English between 2008 and 2017. We grouped lifestyle interventions targeting high-risk individuals by delivery method and personnel type. We used the median incremental cost-effectiveness ratio (ICER), measured in cost per quality-adjusted life year (QALY) or cost saved to measure the CE of interventions. We used the $50,000/QALY threshold to determine ctives. Evaluations of other population-based interventions-including fruit and vegetable subsidies, community-based education programs, and modifications to the built environment-showed inconsistent results.

Most of the T2D prevention interventions included in our review were found to be either cost-effective or cost-saving. Our findings may help decision makers set priorities and allocate resources for T2D prevention in real-world settings.

Most of the T2D prevention interventions included in our review were found to be either cost-effective or cost-saving. Our findings may help decision makers set priorities and allocate resources for T2D prevention in real-world settings.

For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. SCH900353 In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic.

This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases.

Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acutta limitations during the onset of a novel, rapidly changing pandemic.

We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated.

This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data.

Clinical data-including demographics, signs, symptoms, comorbidities, and blood test results-and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework.

Imaging features had the strongest impact on the model output, while a combiimaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.

The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination.

The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP.

The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set.

The classifiers that were constructed with three algorithms from 43 CLI which could help clinicians perform early isolation and centralized management of COVID-19 patients.

The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.Chest auscultation is a widely used clinical tool for respiratory disease detection. The stethoscope has undergone a number of transformative enhancements since its invention, including the introduction of electronic systems in the last two decades. Nevertheless, stethoscopes remain riddled with a number of issues that limit their signal quality and diagnostic capability, rendering both traditional and electronic stethoscopes unusable in noisy or non-traditional environments (e.g. emergency rooms, rural clinics, ambulatory vehicles). This work outlines the design and validation of a low-cost electronic stethoscope that dramatically reduces external noise contamination through hardware redesign and real-time, dynamic signal processing. The proposed system takes advantage of a unique acoustic sensor array, an external facing microphone, and on-board processing to perform adaptive noise suppression. The proposed system is objectively compared to six commercially-available devices in varying levels of simulated noisy clinical settings and quantified using two metrics that reflect perceptual audibility and statistical similarity, normalized covariance measure (NCM) and magnitude squared coherence (MSC).