Advances in heart failure clinical research based on deep learning / 生物医学工程学杂志

Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.

Physician satisfaction with electronic medical record in a huge hospital (China).

Electronic medical record (EMR) is widely used in Chinese hospitals. Yet little is known about the physician satisfaction with EMR in Chinese hospitals. The purpose of this paper is to develop an instrument to measure physician satisfaction with EMR and to survey physician satisfaction with EMR in the biggest hospital in China. We developed a questionnaire and did a cross-sectional survey on 100 physicians from July 2010 to November 2010 in the huge hospital in China. A total of 92 completed questionnaires were returned. Our results revealed that although the overall physician satisfaction with EMR was relatively high (70.7%), certain aspects of the service showed some degree of dissatisfaction (function, efficiency, quality).