A unified view of density-based methods for semi-supervised clustering and classification

Performance Evaluation of Hospital Economic Management with the Clustering Algorithm Oriented towards Electronic Health Management

In order to study the clustering algorithm based on density grid, the performance evaluation index system of hospital economic management under the application of electronic health management system is constructed. Firstly, this work designs the basic architecture of electronic health management system, classifies and screens the process of index system of electronic health management system, compares the clustering algorithm based on density grid with the simple clustering algorithm based on density or grid, and then applies it to the performance evaluation index system of hospital economic management. According to the principle of Mitchell scoring method, the expert questionnaire of hospital economic management performance evaluation index system was designed, and Delphi method was used to evaluate the candidate indexes from the three dimensions of right, legitimacy, and urgency. The results show that, compared with simple network clustering algorithm and density clustering algorithm, the clustering algorithm based on density network produces higher purity (94% VS 73% VS 67%) and lower entropy (0.9 VS 1.4 VS 1.54), which effectively saves memory consumption, and the difference is statistically significant (P < 0.05). The core indicators with scores above 4.5 in both dimensions include budget revenue implementation rate, budget expenditure implementation rate, implementation rate of special financial appropriation, asset-liability ratio, hospitalization income cost rate, medical insurance settlement rate, average cost of discharged patients, and drug proportion. The coefficient of variation of the first grade index is between 0.05 and 0.14 and that of the second grade index is between 0.05 and 0.15. Clustering algorithm based on density network has higher purity and lower entropy, which can effectively save memory consumption. The performance evaluation index system of hospital economic management finally determines 6 first-level indexes: budget management, financial fund management, cost management, medical expense management, medical efficiency, medical quality, and 25 second-level indexes.

Predictors of incident viral symptoms ascertained in the era of COVID-19

Background

In the absence of universal testing, effective therapies, or vaccines, identifying risk factors for viral infection, particularly readily modifiable exposures and behaviors, is required to identify effective strategies against viral infection and transmission.

Methods

We conducted a world-wide mobile application-based prospective cohort study available to English speaking adults with a smartphone. We collected self-reported characteristics, exposures, and behaviors, as well as smartphone-based geolocation data. Our main outcome was incident symptoms of viral infection, defined as fevers and chills plus one other symptom previously shown to occur with SARS-CoV-2 infection, determined by daily surveys.

Findings

Among 14, 335 participants residing in all 50 US states and 93 different countries followed for a median 21 days (IQR 10–26 days), 424 (3%) developed incident viral symptoms. In pooled multivariable logistic regression models, female biological sex (odds ratio [OR] 1.75, 95% CI 1.39–2.20, p<0.001), anemia (OR 1.45, 95% CI 1.16–1.81, p = 0.001), hypertension (OR 1.35, 95% CI 1.08–1.68, p = 0.007), cigarette smoking in the last 30 days (OR 1.86, 95% CI 1.35–2.55, p<0.001), any viral symptoms among household members 6–12 days prior (OR 2.06, 95% CI 1.67–2.55, p<0.001), and the maximum number of individuals the participant interacted with within 6 feet in the past 6–12 days (OR 1.15, 95% CI 1.06–1.25, p<0.001) were each associated with a higher risk of developing viral symptoms. Conversely, a higher subjective social status (OR 0.87, 95% CI 0.83–0.93, p<0.001), at least weekly exercise (OR 0.57, 95% CI 0.47–0.70, p<0.001), and sanitizing one’s phone (OR 0.79, 95% CI 0.63–0.99, p = 0.037) were each associated with a lower risk of developing viral symptoms.

Interpretation

While several immutable characteristics were associated with the risk of developing viral symptoms, multiple immediately modifiable exposures and habits that influence risk were also observed, potentially identifying readily accessible strategies to mitigate risk in the COVID-19 era.

Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data

High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments.