Development and validation of an interpretable machine learning model for the early diagnosis of
- 1Department of Pulmonary and Critical Care Medicine, First Hospital of Changsha, Changsha, 410005, China
- 2Department of Pulmonary and Critical Care Medicine, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, 410005, China
- 3Department of Pulmonary and Critical Care Medicine, First People's Hospital of Chenzhou, Chenzhou, 423000, China
Abstract
Background: Pneumocystis jirovecii pneumonia (PJP) is a common, potentially fatal infection in immunosuppressed patients. Rapid and accurate diagnosis of PJP remains challenging, as delays increase mortality. This study aimed to develop a machine-learning model using non-invasive, easily accessible data to quickly assess PJP risk in these patients. Methods: A multicenter retrospective study was conducted among immunosuppressed patients with pneumonia who underwent bronchoalveolar lavage evaluation between January 1, 2018, and December 31, 2021. Clinical baseline data from patients diagnosed with PJP or non-PJP across 10 hospitals were included. After data preprocessing, patients were divided into training and validation cohorts according to the admission time. After feature selection, a model was developed. The model’s performance was assessed using the imputed datasets from the validation cohort. The optimized model was interpreted using SHapley Additive exPlanations. Results: The 359 enrolled patients included 189 with PJP and 170 with non-PJP pneumonia. Of these, 271 were assigned to the training set for model development, while 88 formed the validation set for performance assessment, respectively. Based on the ranking of feature importance, 15 features were selected for model development. The CatBoost model, selected after tuning, achieved an accuracy of 81.8% (95% CI: 73.5%–90.1%), sensitivity of 82.9% (95% CI, 70.0–95.9%), and specificity of 81.1% (95% CI: 70.2%–91.9%). Conclusions: We constructed a high-performing diagnostic model for the rapid, early diagnosis of PJP in immunosuppressed patients using readily available clinical data within 24 hours of hospital admission. This tool may assist clinicians in evaluating PJP probability in immunocompromised pneumonia patients.
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BibTeX
@article{li2026development,
title = {{Development and validation of an interpretable machine learning model for the early diagnosis of}},
author = {Yantong Li and Hao Zhou and Lu Bai and Rongjun Wan and Juan Jiang and Wenzhong Peng and Pinhua Pan and Chengping Hu and Yuling Tang and Jianmin Li and Qingkai Luo and Wei Yang and Yuanyuan Li},
journal = {Infectious Medicine},
year = {2026},
month = nov,
doi = {10.1016/j.imj.2026.100297},
url = {https://doi.org/10.1016/j.imj.2026.100297},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development and validation of an interpretable machine learning model for the early diagnosis of
AU - Yantong Li
AU - Hao Zhou
AU - Lu Bai
AU - Rongjun Wan
AU - Juan Jiang
AU - Wenzhong Peng
AU - Pinhua Pan
AU - Chengping Hu
AU - Yuling Tang
AU - Jianmin Li
AU - Qingkai Luo
AU - Wei Yang
AU - Yuanyuan Li
PY - 2026
DA - 2026/11/08
JO - Infectious Medicine
DO - 10.1016/j.imj.2026.100297
UR - https://doi.org/10.1016/j.imj.2026.100297
AB - Background: Pneumocystis jirovecii pneumonia (PJP) is a common, potentially fatal infection in immunosuppressed patients. Rapid and accurate diagnosis of PJP remains challenging, as delays increase mortality. This study aimed to develop a machine-learning model using non-invasive, easily accessible data to quickly assess PJP risk in these patients. Methods: A multicenter retrospective study was conducted among immunosuppressed patients with pneumonia who underwent bronchoalveolar lavage evaluation between January 1, 2018, and December 31, 2021. Clinical baseline data from patients diagnosed with PJP or non-PJP across 10 hospitals were included. After data preprocessing, patients were divided into training and validation cohorts according to the admission time. After feature selection, a model was developed. The model’s performance was assessed using the imputed datasets from the validation cohort. The optimized model was interpreted using SHapley Additive exPlanations. Results: The 359 enrolled patients included 189 with PJP and 170 with non-PJP pneumonia. Of these, 271 were assigned to the training set for model development, while 88 formed the validation set for performance assessment, respectively. Based on the ranking of feature importance, 15 features were selected for model development. The CatBoost model, selected after tuning, achieved an accuracy of 81.8% (95% CI: 73.5%–90.1%), sensitivity of 82.9% (95% CI, 70.0–95.9%), and specificity of 81.1% (95% CI: 70.2%–91.9%). Conclusions: We constructed a high-performing diagnostic model for the rapid, early diagnosis of PJP in immunosuppressed patients using readily available clinical data within 24 hours of hospital admission. This tool may assist clinicians in evaluating PJP probability in immunocompromised pneumonia patients.
SN - 2772-431X
ER -