Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 = 286,503), and a validation set (N2 = 95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N = 42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations

Cell-specific transcriptome changes in the hypothalamic arcuate nucleus in a mouse deoxycorticosterone acetate-salt model of hypertension

A common preclinical model of hypertension characterized by low circulating renin is the “deoxycorticosterone acetate (DOCA)-salt” model, which influences blood pressure and metabolism through mechanisms involving the angiotensin II type 1 receptor (AT1R) in the brain. More specifically, AT1R within Agouti-related peptide (AgRP) neurons of the arcuate nucleus of the hypothalamus (ARC) has been implicated in selected effects of DOCA-salt. In addition, microglia have been implicated in the cerebrovascular effects of DOCA-salt and angiotensin II. To characterize DOCA-salt effects upon the transcriptomes of individual cell types within the ARC, we used single-nucleus RNA sequencing (snRNAseq) to examine this region from male C57BL/6J mice that underwent sham or DOCA-salt treatment. Thirty-two unique primary cell type clusters were identified. Sub-clustering of neuropeptide-related clusters resulted in identification of three distinct AgRP subclusters. DOCA-salt treatment caused subtype-specific changes in gene expression patterns associated with AT1R and G protein signaling, neurotransmitter uptake, synapse functions, and hormone secretion. In addition, two primary cell type clusters were identified as resting versus activated microglia, and multiple distinct subtypes of activated microglia were suggested by sub-cluster analysis. While DOCA-salt had no overall effect on total microglial density within the ARC, DOCA-salt appeared to cause a redistribution of the relative abundance of activated microglia subtypes. These data provide novel insights into cell-specific molecular changes occurring within the ARC during DOCA-salt treatment, and prompt increased investigation of the physiological and pathophysiological significance of distinct subtypes of neuronal and glial cell types

Noncoding RNA in Oncogenesis: A New Era of Identifying Key Players

New discoveries and accelerating progresses in the field of noncoding RNAs (ncRNAs) continuously challenges our deep-rooted doctrines in biology and sometimes our imagination. A growing body of evidence indicates that ncRNAs are important players in oncogenesis. While a stunning list of ncRNAs has been discovered, only a small portion of them has been examined for their biological activities and very few have been characterized for the molecular mechanisms of their action. To date, ncRNAs have been shown to regulate a wide range of biological processes, including chromatin remodeling, gene transcription, mRNA translation and protein function. Dysregulation of ncRNAs contributes to the pathogenesis of a variety of cancers and aberrant ncRNA expression has a high potential to be prognostic in some cancers. Thus, a new cancer research era has begun to identify novel key players of ncRNAs in oncogenesis. In this review, we will first discuss the function and regulation of miRNAs, especially focusing on the interplay between miRNAs and several key cancer genes, including p53, PTEN and c-Myc. We will then summarize the research of long ncRNAs (lncRNAs) in cancers. In this part, we will discuss the lncRNAs in four categories based on their activities, including regulating gene expression, acting as miRNA decoys, mediating mRNA translation, and modulating protein activities. At the end, we will also discuss recently unraveled activities of circular RNAs (circRNAs)

Why Doctors Participate in Teams of Online Health Communities? A Social Identity and Brand Resource Perspective

Virtual teamwork has emerged as a new mode of healthcare service in online health communities, enabling doctors to collaborate and share knowledge for better patient care. However, the multifaceted impact of teams on their members and how they develop within them have not been effectively validated. To address this gap, we draw insights from social identity theory and brand resources to understand how doctors benefit from online teams using a unique dataset of 2,222 teams with 4,587 doctors from a large Chinese OHC. Our analysis shows that team capital positively affects members’ performance through warmth and ethics images, while team capital is negatively related to warmth image but positively related to ethics image. Additionally, leaders’ ethics images have a positive influence on members’ ethics images, with mediation effects present. This study sheds light on the role of doctor images in improving their performance in OHCs based on social identity theory and provides practical guidance for online medical services

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 ​= ​286,503), and a validation set (N2 ​= ​95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N ​= ​42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations

More is better? Understanding the effects of online interactions on patients health anxiety

Online health platforms play an important role in chronic disease management. Patients participate in online health platforms to receive and provide health-related support from each other. However, there remains a debate about whether the influence of social interaction on patient health anxiety is linearly positive. Based on uncertainty, information overload, and the theory of motivational information management, we develop and test a model considering a potential curvilinear relationship between social interaction and health anxiety, as well as a moderating effect of health literacy. We collect patient interaction data from an online health platform based on chronic disease management in China and use text mining and econometrics to test our hypotheses. Specifically, we find an inverted U-shaped relationship between informational provision and health anxiety. Our results also show that information receipt and emotion provision have U-shaped relationships with health anxiety. Interestingly, health literacy can effectively alleviate the U-shaped relationship between information receipt and health anxiety. These findings not only provide new insights into the literature on online patient interactions but also provide decision support for patients and platform managers