ABSTRACT
It is noteworthy that only 5% of more than 7000 described rare diseases are treated. In the era of big data, there is ever-increasing data for understanding biomedicine. The need for efficient and rapid data collection, analyses and characterization methods is pressing. Rare diseases can particularly benefit from artificial intelligence (AI) application. AI, with an emphasis on machine learning, creates a path for such efforts and is being applied to diagnosis and treatment. AI has demonstrated its potential to learn and analyze data from different sources with results in prediction。Presently, there are AI-driven technologies applied for rare diseases and this review aims to summarize these advances. Moreover, this review scrutinizes the limitation and identifies the pitfalls of AI applications in the diagnosis and treatment of rare diseases.
ABSTRACT
Objective:To explore a new model of Total Quality Management in project management of National Natural Science Foundation of China (NSFC).Methods:The theory of Total Quality Management is applied to the management of NSFC funded project, that is, a complete and comprehensive process formed which covered the different stages of project cultivation, application, identification, implementation, completion and post-completion tracking; and the management concepts of whole process personnel engagement and comprehensive quality management are penetrated into those different stages, furthermore, corresponding key points should be focused are also analyzed.Results:The hospital has made great achievements in scientific research management, project quantity, outputs, and discipline construction.Conclusions:The introduction of Total Quality Management theory into NSFC management is conducive to continuously improve the level of fund management, and enhance the scientific and technological innovation capacity building of the hospital.
ABSTRACT
Objective To provide a system for warning, preventing and controlling emerging infectious diseases from a macroscopic perspective, using the COVID-19 epidemic data and effective distance model. Methods The dates of hospitalization/isolation treatment of the first confirmed cases of COVID-19 and the cumulative numbers of confirmed cases in different provinces in China reported as of 23 February, 2020 were collected. The Location Based Service (LBS) big data platform of 'Baidu Migration' was employed to obtain the data of the proportion of the floating population from Wuhan to all parts of the country. Effective distance models and linear regression models were established to analyze the relationship between the effective distance and the arrival time of the epidemic as well as the number of cumulative confirmed cases at provincial and municipal levels. Results The arrival time of the epidemic and the cumulative number of confirmed cases of COVID-19 had significant linear relationship at both provincial and municipal levels in China, and the regression coefficients of each linear model were significant ( P <0.001). At the provincial level, the effective distance could explain about 71% of the variation of the model with arrival time along with around 90% of the variation for the model in the cumulative confirmed case magnitude; at the municipal level, the effective distance could explain about 66% of the variation for the model in arrival time, and about 85% of the variation of the model with the cumulative confirmed case magnitude. Conclusions The fitting degree of the models are good. The LBS big data and effective distance model can be used to estimate the track, time and extent of epidemic spread to provide useful reference for early warning, prevention and control of emerging infectious diseases.