Efficacy and Safety of Sea Salt-Derived Physiological Saline Nasal Spray as Add-On Therapy in Patients with Acute Upper Respiratory Infection: A Multicenter Retrospective Cohort Study.

BACKGROUND The purpose of this study was to assess the effects of seawater on nasal congestion and runny nose symptoms in adults with an acute upper respiratory infection (URI). MATERIAL AND METHODS This was a multicenter retrospective cohort trial of patients with acute URI and symptoms of nasal congestion and runny nose. The patients were assigned to 2 groups and were administered regular non-drug supportive treatment or supportive treatment with nasal irrigation with sea salt-derived physiological saline. The primary efficacy endpoint was the effective rate (percentage of patients with ≥30% symptom score reduction from baseline for nasal congestion and runny nose). RESULTS In total, 144 patients were enrolled, including 72 in each group, and 143 patients completed the study. Both groups had similar demographics and vital signs. The effective rates for nasal congestion and runny nose were significantly increased in the seawater group compared with patients in the control group (87.3% vs 59.7% for nasal congestion; 85.9% vs 61.1% for runny nose; both P<0.001). In addition, the 2 groups showed markedly different degrees of patient symptom score improvement in sleep quality and appetite (both P<0.01), but not in cough and fatigue (both P>0.05). There were no adverse events in either group. CONCLUSIONS The sea salt-derived physiological saline nasal spray device satisfactorily improved nasal congestion, runny nose, sleep quality, and appetite in adults with URI, with no adverse effects.

On mining latent topics from healthcare chat logs.

BACKGROUND: Public and internet-based social media such as online healthcare-oriented chat groups provide a convenient channel for patients and people concerned about health to communicate and share information with each other. The chat logs of an online healthcare-oriented chat group can potentially be used to extract latent topics, to encourage participation, and to recommend relevant healthcare information to users. OBJECTIVE: This paper addresses the use of online healthcare chat logs to automatically discover both underlying topics and user interests. METHOD: We present a new probabilistic model that exploits healthcare chat logs to find hidden topics and changes in these topics over time. The proposed model uses separate but associated hidden variables to explore both topics and individual interests such that it can provide useful insights to the participants of online healthcare chat groups about their interests in terms of weighted topics or vice versa. RESULTS: We evaluate the proposed model on a real-world chat log by comparing its performance to benchmark topic models, i.e., latent Dirichlet allocation (LDA) and Author Topic Model (ATM), on the topic extraction task. The chat log is obtained from an online chat group of pregnant women, which consists of 233,452 chat word tokens contributed by 118 users. Both detected individual interests and underlying topics with their progressive information over time are demonstrated. The results show that the performance of the proposed model exceeds that of the benchmark models. CONCLUSION: The experimental results illustrate that the proposed model is a promising method for extracting healthcare knowledge from social media data.

Discovery of clinical pathway patterns from event logs using probabilistic topic models.

Discovery of clinical pathway (CP) patterns has experienced increased attention over the years due to its importance for revealing the structure, semantics and dynamics of CPs, and to its usefulness for providing clinicians with explicit knowledge which can be directly used to guide treatment activities of individual patients. Generally, discovery of CP patterns is a challenging task as treatment behaviors in CPs often have a large variability depending on factors such as time, location and patient individual. Based on the assumption that CP patterns can be derived from clinical event logs which usually record various treatment activities in CP executions, this study proposes a novel approach to CP pattern discovery by modeling CPs using mixtures of an extension to the Latent Dirichlet Allocation family that jointly models various treatment activities and their occurring time stamps in CPs. Clinical case studies are performed to evaluate the proposed approach via real-world data sets recording typical treatment behaviors in patient careflow. The obtained results demonstrate the suitability of the proposed approach for CP pattern discovery, and indicate the promise in research efforts related to CP analysis and optimization.

Mining the changes of medical behaviors for clinical pathways.

In a fast-changing healthcare environment, understanding the changes of medical behaviors in clinical pathways can help hospital managers improve the pathways and make better medical strategies for patient careflow. In this study we propose an approach to detect medical behavior changes between two time periods, by providing a change pattern detection algorithm dividing the discovered change patterns into four categories (i.e., perished patterns, added patterns, unexpected changes, and emerging patterns). The proposed approach is evaluated via real-world data sets extracted from Zhejiang Huzhou Central Hospital of China with regard to the clinical pathway of bronchial lung cancer in 2007-2009 and 2011. The experiment results include three categories of change patterns from the collected data-sets, making a relatively comprehensive cover on the significant changes in clinical pathways, which might be essential from the perspectives of clinical pathway analysis and improvement.