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BackgroundWith the rapid development of online health communities, increasing numbers of patients and families are seeking health information on the internet. ObjectiveThis study aimed to discuss how to fully reveal the health information needs expressed by patients with hypertension in their questions in a web-based environment and how to use the internet to help patients with hypertension receive personalized health education. MethodsThis study randomly selected 1000 text records from the question data of patients with hypertension from 2008 to 2018 collected from Good Doctor Online and constructed a classification system through literature research and content analysis. This paper identified the background characteristics and questioning intention of each patient with hypertension based on the patient’s question and used co-occurrence network analysis and the k-means clustering method to explore the features of the health information needs of patients with hypertension. ResultsThe classification system for the health information needs of patients with hypertension included the following nine dimensions: drugs (355 names), symptoms and signs (395 names), tests and examinations (545 names), demographic data (526 kinds), diseases (80 names), risk factors (37 names), emotions (43 kinds), lifestyles (6 kinds), and questions (49 kinds). There were several characteristics of the explored web-based health information needs of patients with hypertension. First, more than 49% of patients described features, such as drugs, symptoms and signs, tests and examinations, demographic data, and diseases. Second, patients with hypertension were most concerned about treatment (778/1000, 77.80%), followed by diagnosis (323/1000, 32.30%). Third, 65.80% (658/1000) of patients asked physicians several questions at the same time. Moreover, 28.30% (283/1000) of patients were very concerned about how to adjust the medication, and they asked other treatment-related questions at the same time, including drug side effects, whether to take the drugs, how to treat the disease, etc. Furthermore, 17.60% (176/1000) of patients consulted physicians about the causes of clinical findings, including the relationship between the clinical findings and a disease, the treatment of a disease, and medications and examinations. Fourth, by k-means clustering, the questioning intentions of patients with hypertension were classified into the following seven categories: “how to adjust medication,” “what to do,” “how to treat,” “phenomenon explanation,” “test and examination,” “disease diagnosis,” and “disease prognosis.” ConclusionsIn a web-based environment, the health information needs expressed by Chinese patients with hypertension to physicians are common and distinct, that is, patients with different background features ask relatively common questions to physicians. The classification system constructed in this study can provide guidance to health information service providers for the construction of web-based health resources, as well as guidance for patient education, which could help solve the problem of information asymmetry in communication between physicians and patients.

As the basic units of the human body structure and function, cells have a considerable influence on maintaining the normal work of the human body. In medical diagnosis, cell examination is an important part of understanding the human function. Incorporating cell examination into medical diagnosis would greatly improve the efficiency of pathological research and patient treatment. In addition, cell segmentation and identification technology can be used to quantitatively analyze and study cellular components at the molecular level. It is conducive to the study of the pathogenesis of diseases and to the formulation of highly effective disease treatment programs. However, because cells are of diverse types, their numbers are huge, and they exist in the order of micrometers, detecting and identifying cells without using a deep learning-based computer program are extremely difficult. Therefore, the use of computers to study and analyze cells has a certain practical value. In this work, target detection theory using deep learning is applied to cell detection. A target recognition network model is built based on the faster region-based convolutional neural network (R-CNN) algorithm, and the anchor box is designed in accordance with the characteristics of the data set. Different design methods influence cell detection results. Using the object detection method based on our novel faster R-CNN framework to detect the cell image can help improve the speed and accuracy of cell detection. The method has considerable advantages in dealing with the identification of flowing cells.