Computer methods and programs in biomedicine · 2025
Non-experimental rapid identification of lower respiratory tract infections in patients with chronic obstructive pulmonary disease using multi-label learning.
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He Hangzhi, Zhao Hui, Li Lifang, Yang Hong, Yan Jingjing, Yuan Yiwei, Hu Xiangwen, Zhang Yanbo
Аннотация
BACKGROUND AND OBJECTIVE: Microbiological culture is a standard diagnostic test that takes a long time to identify lower respiratory tract infections (LRTI) in patients with chronic obstructive pulmonary disease (COPD). This study entailed the development of an interactive decision-support system using multi-label machine learning. It is designed to assist clinical medical staff in the rapid and simultaneous diagnosis of various infections in these patients.
METHODS: Clinical health record data were collected from inpatients with COPD suspected of having a LRTI. Two major categories of multi-label learning frameworks were integrated with various machine learning algorithms to create 23 predictive models to identify four categories of infection: fungal, gram-negative bacterial, gram-positive bacterial, and multidrug-resistant organism infections. The predictive power of the individual models was tested. Subsequently, the model with the highest comprehensive performance was selected and integrated with SHAP technology to construct a decision support system.
RESULTS: Three-thousand-eight-hundred-one subjects participated in this study. LP-RF recorded the highest overall performance, with a Hamming loss of 0.158 (95 %CI: 0.157-0.159) and a samples-precision of 0.894 (95 %CI: 0.891-0.896). The developed diagnostic decision support system generates predicted probability output for each infection category in a specific patient and displays the interpreted output results.
CONCLUSION: The developed multi-label decision support system enables effective prediction of four categories of infections in patients with a history of COPD, and has the potential to curb the overuse of antimicrobial drugs. This system is highly explainable and interactive, providing real-time support in the simultaneous diagnosis of multiple infection categories.
METHODS: Clinical health record data were collected from inpatients with COPD suspected of having a LRTI. Two major categories of multi-label learning frameworks were integrated with various machine learning algorithms to create 23 predictive models to identify four categories of infection: fungal, gram-negative bacterial, gram-positive bacterial, and multidrug-resistant organism infections. The predictive power of the individual models was tested. Subsequently, the model with the highest comprehensive performance was selected and integrated with SHAP technology to construct a decision support system.
RESULTS: Three-thousand-eight-hundred-one subjects participated in this study. LP-RF recorded the highest overall performance, with a Hamming loss of 0.158 (95 %CI: 0.157-0.159) and a samples-precision of 0.894 (95 %CI: 0.891-0.896). The developed diagnostic decision support system generates predicted probability output for each infection category in a specific patient and displays the interpreted output results.
CONCLUSION: The developed multi-label decision support system enables effective prediction of four categories of infections in patients with a history of COPD, and has the potential to curb the overuse of antimicrobial drugs. This system is highly explainable and interactive, providing real-time support in the simultaneous diagnosis of multiple infection categories.