As advancements in predictive analytics continue to define how quality care is measured in the health care industry, Baghernejad and Li’s articles depict how machine learning techniques can be utilized in medical and scientific research to better understand key data trends, inputs and nuances all while receiving new and valuable insights.
Baghernejad’s research explores the importance of class-based variables within tree-based modeling in Class Based Variable Importance for Medical Decision Making, analyzing the effects these variables can have on medical interference and action ability.
Throughout this research, my goal was to draw conclusions on how to bridge gaps that exist between medical prediction and interference, as well as validate how important variables are when it comes to creating useful measurements that will ultimately lead to further understanding in machine learning,” said Baghernejad.
Li analyzes the usefulness of machine learning when it comes to microbiome-based diagnostics in her article, Machine Learning Techniques on Microbiome-Based Diagnostics.
Machine learning and statistical techniques in human microbiome data can be used to address the complex mechanisms underlying disease,” said Li. “In this article, I wanted to illustrate how machine learning approaches provide credible starting points for further research on microbiome-based diagnostics to identify specific disease-associated microbial communities.”
Since creating its analytics business unit in 2015, Intermedix has worked to help providers and various organizations understand trends and make informed decisions through reliable data. The company has placed a heavy emphasis on developing its analytics research and development efforts—especially in regards to its machine learning capabilities.
Both Baghernejad and Li, who are members of the Intermedix data science team, have dedicated a majority of their time and energy toward making new discoveries in this area. Their research work has been primarily focused on how to implement machine learning ideas to various industry settings, close any gaps that exist between technical and non-technical teams and augment the collective intelligence that exists with subject matter experts throughout Intermedix.
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