arXiv subjects

Ayman Alahmar

Publications and source records attributed to Ayman Alahmar.

3 recordsLinked to original sources

arXiv2022

Rust: The Programming Language for Safety and Performance

Rust is a young programming language gaining increased attention from software developers since it was introduced to the world by Mozilla in 2010. In this study, we attempt to answer several research questions. Does Rust deserve such increased attention? What is there in Rust that is attracting programmers to this new language? Safety and performance were among the very first promises of Rust, as was claimed by its early developers. Is Rust a safe language with high performance? Have these claims been achieved? To answer these questions, we surveyed and analyzed recent research on Rust and research that benchmarks Rust with other available prominent programming languages. The results show that Rust deserves the increased interest by programmers, and recent experimental results in benchmarking research show Rust's overall superiority over other well-established languages in terms of performance, safety, and security. Even though this study was not comprehensive (and more work must be done in this area), it informs the programming and research communities on the promising features of Rust as the language of choice for the future.

cs.PL↗

arXiv2022

Computerization of Clinical Pathways: A Literature Review and Directions for Future Research

Clinical Pathways (CP) are medical management plans developed to standardize patient treatment activities, optimize resource usage, reduce expenses, and improve the quality of healthcare services. Most CPs currently in use are paper-based documents (i.e., not computerized). CP computerization has been an active research topic since the inception of CP use in hospitals. This literature review research aims to examine studies that focused on CP computerization and offers recommendations for future research in this important research area. Some critical research suggestions include centralizing computerized CPs in Healthcare Information Systems (HIS), CP term standardization using international medical terminology systems, developing a global CP-specific digital coding system, creating a unified CP meta-ontology, developing independent Clinical Pathway Management Systems (CPMS), and supporting CPMSs with machine learning sub-systems.

cs.AI↗

arXiv2021

A Literature Review on Length of Stay Prediction for Stroke Patients using Machine Learning and Statistical Approaches

Hospital length of stay (LOS) is one of the most essential healthcare metrics that reflects the hospital quality of service and helps improve hospital scheduling and management. LOS prediction helps in cost management because patients who remain in hospitals usually do so in hospital units where resources are severely limited. In this study, we reviewed papers on LOS prediction using machine learning and statistical approaches. Our literature review considers research studies that focus on LOS prediction for stroke patients. Some of the surveyed studies revealed that authors reached contradicting conclusions. For example, the age of the patient was considered an important predictor of LOS for stroke patients in some studies, while other studies concluded that age was not a significant factor. Therefore, additional research is required in this domain to further understand the predictors of LOS for stroke patients.

cs.LG↗