Development and validation of a machine learning framework for improved resource allocation in the emergency department.

Objective: The Emergency Severity Index (ESI) is the most commonly used system in over 70% of all U.S. emergency departments (ED) that uses predicted resource utilization as a means to triage [1], Mistriage, which includes both undertriage and overtriage has been a persistent issue, affecting 32.

Impact of medical technologies may be predicted using constructed graph bibliometrics.

Scientific research is driven by allocation of funding to different research projects based in part on the predicted scientific impact of the work. Data-driven algorithms can inform decision-making of scarce funding resources by identifying likely high-impact studies using bibliometrics. Compared to standardized citation-based metrics alone, we utilize a machine learning pipeline that analyzes high-dimensional relationships among a range of bibliometric features to improve the accuracy of predicting high-impact research.

Prevalence of Sickle Cell Trait in the Southern Suburb of Beirut, Lebanon.

Objective: The objective of this study was to assess the prevalence, gender differences, and time trends of Sickle Cell Trait in the Southern Suburb of Beirut, Lebanon, as well as to highlight the importance of screening for Sickle Cell Trait carriers in this population. Another objective was to describe a new screening technique for Sickle Cell Trait carriers.

Methods: This was a retrospective cohort study carried out at a private laboratory in the Southern Suburb of Beirut, Lebanon between 2002 and 2014.