Discussion Topic for Seminar 6 (Presented by Preut

[PREUT ASSAWAWORRARIT]

Article: Identifying predictors associated with risk of death of admission to intensive care unit in internal medicine patients with sepsis: A comparison of statistical models and machine learning algorithm.

1. Would you like to apply these results in your hospital, why and why not?

2. Which models/algorithm will you consider to apply in your hospital?

[Zarni Lynn Kyaw]

For the 1st question,

I would definitely like to apply these results in my hospital. Sepsis is a very serious condition, and it is important to do everything we can to prevent deaths and admissions to the ICU.

Specifically, I would use these results to develop a risk stratification tool for patients with sepsis. This tool would allow us to identify patients who are at high risk of poor outcomes, so that we can provide them with more aggressive treatment and monitoring. I would also use these results to educate the staff about the risk factors for sepsis, so that they can be more vigilant in identifying and treating patients who are at risk.

I believe that applying these results would have a significant impact on the outcomes of patients with sepsis in my hospital. We would be able to identify patients who are at high risk of poor outcomes and provide them with the care they need to improve their chances of survival. This would ultimately lead to fewer deaths and admissions to the ICU.

The findings of this study could also be used to improve the quality of care for patients with sepsis in other hospitals.

For 2nd question,
I would consider the following

Random forests: This algorithm is relatively easy to interpret, which is important for clinical decision-making.

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