Optimizing home visits through machine learning for preventing peritoneal dialysis-associated peritonitis: a proof of concept study and results from PDOPPS.

This article discusses a proof of concept study that explores the use of machine learning algorithms to optimize home visits for preventing peritoneal dialysis-associated peritonitis. The study used data from the Thai Peritoneal Dialysis Outcome and Practice Patterns Study (PDOPPS) to train the algorithm and generate a risk-based patient visit sequence. The algorithm demonstrated moderate discrimination ability in distinguishing between high-risk and low-risk patients. However, there were limitations to the study, such as missing data and uncertainty regarding the effectiveness of home visits in reducing peritonitis risk. Further research is needed to enhance the predictive accuracy and practical utility of the machine learning algorithm in clinical settings. [Extracted from the article]

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