Publications by authors named "Kyogo Wagatsuma"
Article Synopsis
- Machine learning, particularly the random forest classifier, is being utilized to help manage the dry weight of patients undergoing hemodialysis, a task that involves complex decision-making based on multiple health indicators.
- A study involving 69,375 dialysis records from 314 Asian patients demonstrated that the classifier could effectively predict dry weight adjustments, showing areas under the curve of 0.70 and 0.74 for upward and downward adjustments, respectively.
- Key indicators for dry weight adjustments were identified: a decline in median blood pressure correlated with upward adjustments, while elevated C-reactive protein and hypoalbuminemia were linked to downward adjustments, suggesting potential clinical applications for improving patient care.
