Reaching high-risk patient populations through emergency department opt-out HIV testing: A retrospective chart review

Specific Aims and Hypotheses: This study aimed to identify socioeconomic (SE), sexual, and other risk factors (RFs), among patients diagnosed with HIV infection through an emergency department-based opt-out HIV screening program, and to examine trends in intravenous drug use (IVDU) as a RF. H1: Unsafe sexual practices are the most commonly reported RF. H2: Role of IVDU as a RF has increased over the time period studied. Poster presented at 2017 APHA conference in Atlanta Georgia.https://jdc.jefferson.edu/cwicposters/1040/thumbnail.jp

Obstacles and Challenges to Implementing Multi-departmental QI at a Large, Academic Training Center-Lessons Learned from a HCV Screening Program

Objectives: We aimed to double the HCV screening rate of ‘baby-boomers’ admitted to the medicine teaching service at Methodist Hospital over the course of 6 months and demonstrate improved linkage to care for HCV RNA+ individuals. Initial efforts were a collaboration between Emergency Medicine, where faculty had experience implementing an HIV screening program, and Gastroenterology, a key stakeholder in linkage to care. Our pilot period coincided with new state regulations mandating that hospitals implement HCV screening for inpatients. These new regulations dramatically altered the scope and goals of the project.https://jdc.jefferson.edu/patientsafetyposters/1030/thumbnail.jp

Use of EPIC EMR for Early Identification and Management of Patients at Risk of Cardiac Implantable Electronic Device (CIED) Infection

Objectives Aim of our project was early identification of 100% of patients with a CIED IMPLANT presenting with bacteremia Process involves use of EPIC EMR to automatically identify patients with positive blood cultures Traditionally, cardiologists are alerted by the care team using the CONSULT system for management of these patients EPIC EMR as an adjunct to the CONSULT syste

Using GMM in Open Cluster Membership: An Insight

The unprecedented precision of Gaia has led to a paradigm shift in membership determination of open clusters where a variety of machine learning (ML) models can be employed. In this paper, we apply the unsupervised Gaussian Mixture Model (GMM) to a sample of thirteen clusters with varying ages ($log \ t \approx$ 6.38-9.64) and distances (441-5183 pc) from Gaia DR3 data to determine membership. We use ASteca to determine parameters for the clusters from our revised membership data. We define a quantifiable metric Modified Silhouette Score (MSS) to evaluate its performance. We study the dependence of MSS on age, distance, extinction, galactic latitude and longitude, and other parameters to find the particular cases when GMM seems to be more efficient than other methods. We compared GMM for nine clusters with varying ages but we did not find any significant differences between GMM performance for younger and older clusters. But we found a moderate correlation between GMM performance and the cluster distance, where GMM works better for closer clusters. We find that GMM does not work very well for clusters at distances larger than 3~kpc.Comment: Accepted in Astronomy & Computin

Membership of Stars in Open Clusters using Random Forest with Gaia Data

Membership of stars in open clusters is one of the most crucial parameters in studies of star clusters. Gaia opened a new window in the estimation of membership because of its unprecedented 6-D data. In the present study, we used published membership data of nine open star clusters as a training set to find new members from Gaia DR2 data using a supervised random forest model with a precision of around 90\%. The number of new members found is often double the published number. Membership probability of a larger sample of stars in clusters is a major benefit in determination of cluster parameters like distance, extinction and mass functions. We also found members in the outer regions of the cluster and found sub-structures in the clusters studied. The color magnitude diagrams are more populated and enriched by the addition of new members making their study more promising.Comment: Accepted for publication in The European Physical Journal ST, Special Issue on Modeling Machine Learning and Astronom