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 stratification model with high recall and clinical interpretability, which was based on a multiclass logistic regression algorithm. Finally, through a 10-fold cross-validation to quantitatively evaluate the risk stratification ability of the model, a total of 2243 clinical data consisting of 36 dimension clinical features from fever clinics were used for training and evaluation of the model.
Results
DDC19 is composed of three parts: mobile terminal apps for the patient-end and GP-end, and the database system. All mobile terminal devices were wirelessly connected to the back end data center to implement request sending and data transmission. We used low risk, moderate risk, and high risk as labels, and adopted a 10-fold cross-validation method to evaluate and test the COVID-19 dynamic risk stratification model in different scenarios (different dimensions of personal clinical data accessible at an earlier stage). The data set dimensions were (2243, 15) when only using the data of patients’ demographic information, 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.
This pilot study demonstrated that machine learning algorithms are potential tools for the evaluation and prediction of noise-induced hearing impairment in workers exposed to diverse complex industrial noises.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
ObjectivesLong non-coding RNAs (lncRNAs) are playing important roles in cancer progression and metastasis. Recent studies have demonstrated that the lncRNA, nuclear paraspeckle assembly transcript 1 (NEAT1), was aberrantly up-regulated in various types of cancers and was reported to be associated with unfavorable prognosis in cancer patients. This study examined the relationship between NEAT1 and relevant clinical outcomes.ResultsA total of 1354 patients from 11 eligible studies were included in the meta-analysis. The results showed that high expression level of NEAT1 was significantly associated with shorter overall survival in cancer patients (hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.36–1.71); in the subgroup analysis, the positive association was also found in patients with hepato-gastroenterol cancers (HR = 1.79, 95% CI = 1.48–2.16), non-small cell lung cancer (HR = 1.35, 95% CI = 1.04–1.76), ovarian cancer (HR = 1.41, 95% CI = 1.11–1.79) and other types of cancers (HR = 1.42, 95% CI = 1.11–1.81). The clinicopathological parameters analysis further showed that increased expression level of NEAT1 was positively correlated with larger tumor size (odds ratio (OR) = 1.74, 95% CI = 1.26–2.41), lymph node metastasis (OR = 2.29, 95% CI = 1.71–3.06), advanced TNM stage (OR = 3.60, 95% CI = 2.27–5.72), poor tumor differentiation (OR = 2.16, 95% CI = 1.58–2.93), distant metastasis (OR = 3.51, 95% CI = 1.75–7.01), and invasion depth (OR = 1.94, 95% CI = 1.36–2.75).Materials and MethodsA comprehensive search was performed in Pubmed, Embase, Web of Science and CNKI databases, and eligible studies were included based on defined exclusion and inclusion criteria to perform meta-analysis.ConclusionsThe meta-analysis results from present study suggested that increased expression level of NEAT1 was associated with unfavorable prognosis and may serve as a predictive factor for clinicopathological features in various cancers.
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