This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections.

Men who have sex with men (MSM) are at disproportionally high risk for human immunodeficiency virus (HIV) infection in China. The increasing HIV prevalence among MSM highlights the urgent need for effective prevention interventions among MSM. Interventions targeted at individuals who are highly vulnerable to HIV infection have been proven effective in reducing incidence rates. However, existing targeted interventions are limited to small-scale programs. To investigate the effectiveness of large-scale targeted network interventions in real-world settings, we build a stochastic agent-based network model informed by the comprehensive online social networking and dating behavior data and epidemiological data among MSM in Zhuhai, China. With the proposed model, we simulate HIV transmissions and compare the efficacy of different targeted intervention programs. We propose a new method, namely, RiskRank, to prioritize nodes for targeted interventions by incorporating: 1) their topological features on the online social network; 2) the underlying epidemic dynamics; and 3) the position of identified HIV-infected individuals on the sexual network. Results show that the targeted interventions are more effective than random interventions in large-scale HIV epidemic control. The proposed RiskRank method consistently outperforms state-of-the-art baselines in various intervention scenarios.

Given the severe impact of low bone density (LBD) on public health, and to avoid the potential damage of X-rays-based bone density measurements, this study aimed to develop a scoring system for the mass screening for LBD in women aged 50 years or older, using the basic body check-up items as variables. Five variables, including age, body mass index (BMI), systolic blood pressure (SBP), blood glucose level, and total cholesterol level (TCL), were obtained from medical check-up records of 1525 women aged 50 years or older who had done body examination between 2011 and 2018, and were used to construct a scoring system for the screening for LBD. Multivariate logistic regression was applied to investigate the putative association of the five variables with LBD. A scoring system was derived from the regression model to discriminate persons at risk of LBD from low-risk persons. An artificial neural network (ANN) model was also applied to the same task. Precision, recall,$F1$-score, and c-statistic were adopted as evaluation metrics. Age, BMI, SBP, glucose, and TCL were significantly associated with the risk of LBD. Precision, recall, c-statistic, and$F1$-score of the proposed scoring system were 0.66, 0.83, 0.73, and 0.74, respectively. ANNs achieved better performances in terms of all measurements. This study demonstrates the feasibility of using routine body check-up items to estimate LBD risk. Different from X-rays-based instruments, the scoring system derived from this study may serve as a postcheck-up mass screening tool to enable health practitioners to identify individuals at a risk of LBD efficiently and nonintrusively.

In a community question-answering (CQA) system, the answer selection task is used to identify the best answer for a specific question. This plays a key role in improving service quality by recommending appropriate answers to new questions. Recent advances in CQA answer selection have focused on enhancing performance by incorporating community information, and particularly the expertise (previous answers) and authority (position in the social network) of a respondent. However, existing approaches to incorporating this information are limited, as they (a) consider either the expertise or the authority, but not both; (b) ignore domain knowledge that could differentiate between the topics of previous answers; or (c) simply use authority information to adjust the similarity score, rather than fully integrating it into the process of measuring the similarity between the question and answer segments. We propose a new approach called the knowledge-enhanced attentive answer selection (KAAS) model, which enhances performance by (a) considering both the expertise and the authority of the answerer; (b) utilizing human-labeled tags, a taxonomy of tags, and votes as domain knowledge to infer the expertise of the respondent; (c) using a matrix decomposition of the social network (based on ‘following’ relationships) to infer the authority of the respondent and incorporating this information into the process of evaluating the similarity between segments. In addition, we incorporate an external knowledge graph to capture more professional information for CQA systems for vertical communities. We also adopt an attention mechanism to integrate our analysis of both questions and answers texts and the aforementioned community information. Experiments with both vertical and general CQA sites demonstrate the superior performance of the proposed KAAS model.