Manuscript Topics

Data science has emerged as a critical discipline in healthcare due to its ability to combine statistical techniques, informatics, and domain expertise to provide valuable insights into the vast amounts of health data available. The availability of electronic medical records, genomic data, and other clinical data sources has driven the development of new techniques for analysis and modeling in this field.

The use of machine learning, statistical methods, and mathematical models to analyze large-scale health and biomedical datasets has become increasingly popular in recent years. These techniques have the potential to uncover previously unknown patterns and associations that can lead to new discoveries and improve patient outcomes.

In this special issue, we welcome papers that highlight the latest developments in biostatistics and health data science. These papers showcase the application of advanced statistical and machine learning methods, as well as mathematical modeling, to analyze and interpret health and genomics data. These methods enable researchers to develop more accurate predictive models, identify risk factors for specific diseases, and develop more targeted interventions to improve health outcomes.

Topics of interest for this special issue include but are not limited to:

• The use of statistical and machine learning methods in health and genomics data analysis to gain insights into complex health problems.
• The application of mathematical modeling in biomedicine.
• The development of novel computational and statistical methods to analyze and interpret large-scale health and biomedical datasets.

Overall, this special issue aims to showcase the latest advancements in biostatistics and health data science and to promote the use of data-driven approaches in healthcare.

Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/