A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections
Abstract Background In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learning model based on patients’ clinical infor...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-03-01
|
| Series: | BMC Pulmonary Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12890-025-03580-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849390479129444352 |
|---|---|
| author | Yemeng Yang Kun Han Jiatao Li Tao Zhang Zhijing Zhu Ling Su Zhaoyong Han Chunyan Xu Yi Lu Likun Pan Tao Yang |
| author_facet | Yemeng Yang Kun Han Jiatao Li Tao Zhang Zhijing Zhu Ling Su Zhaoyong Han Chunyan Xu Yi Lu Likun Pan Tao Yang |
| author_sort | Yemeng Yang |
| collection | DOAJ |
| description | Abstract Background In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learning model based on patients’ clinical information to predict the clinical effectiveness of Piperacillin-tazobactam (TZP) (4:1) in treating bacterial lower respiratory tract infections (LRTIs), to assist clinicians in making better clinical decisions. Methods We collected data from patients diagnosed with LRTIs or equivalent diagnoses admitted to the Department of Pulmonary and Critical Care Medicine at Shanghai Pudong Hospital, Shanghai, between January 1, 2021, and July 31, 2023. A total of 26 relevant clinical features were extracted from this cohort. Following data preprocessing, we trained four models: Logistic Regression, Random Forest, Support Vector Machine, and Gaussian Naive Bayes. The dataset was split into training and test sets using a 7:3 ratio. The top-performing models, as determined by Receiver Operating Characteristic (ROC)-Area Under the Curve (AUC) on the independent test set, were subsequently ensembled. Ensemble model (EL) performance was evaluated using bootstrap resampling on the training set and ROC-AUC, recall, accuracy, precision, F1-score, and log loss on an independent test set. The optimal model was then deployed as a web application for clinical outcome prediction. Results A total of 1,314 patients primarily treated with TZP as initial empiric antibiotic therapy were enrolled in the analysis. The success group comprised 995 patients (75.7%), while the failure group consisted of 319 patients (24.3%). We constructed an ensemble learning model based on the Logistic Regression, Support Vector Machine and Random Forest models, which showed better overall performance. The EL model demonstrated robust performance on an independent test set, exhibiting a ROC-AUC of 0.69, a recall of 0.69, an accuracy of 0.64, a precision of 0.40, a F1-score of 0.50, and a log loss of 0.66. A corresponding web application was then developed and made available at http://106.12.146.54:1020/ . Conclusions In this study, we successfully developed and validated an EL model that effectively predicts the clinical effectiveness of TZP (4:1) in treating bacterial LRTIs. The model achieved a balanced performance across key evaluation metrics, demonstrating the model’s potential utility in clinical decision-making. The web-based application makes this model readily accessible to clinicians, potentially helping optimize antibiotic dosing decisions and reduce both inadequate treatment and overexposure. While promising, future studies with larger datasets and prospective validation are needed to further improve the model’s performance and validate its clinical utility. This work represents a step forward in using machine learning to support antimicrobial stewardship and personalized antibiotic therapy. |
| format | Article |
| id | doaj-art-bc96e089c5b3418682aca4237cd237a3 |
| institution | Kabale University |
| issn | 1471-2466 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj-art-bc96e089c5b3418682aca4237cd237a32025-08-20T03:41:39ZengBMCBMC Pulmonary Medicine1471-24662025-03-0125111310.1186/s12890-025-03580-6A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infectionsYemeng Yang0Kun Han1Jiatao Li2Tao Zhang3Zhijing Zhu4Ling Su5Zhaoyong Han6Chunyan Xu7Yi Lu8Likun Pan9Tao Yang10Department of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University Pudong Medical CenterShanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal UniversitySchool of Pharmacy, Shanghai University of Medicine & Health SciencesDepartment of Pharmacy, Shanghai Pudong Hospital, Fudan University Pudong Medical CenterSchool of Materials and Chemistry, University of Shanghai for Science and TechnologyDepartment of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University Pudong Medical CenterDepartment of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University Pudong Medical CenterDepartment of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University Pudong Medical CenterDepartment of Pharmacy, Shanghai Pudong Hospital, Fudan University Pudong Medical CenterShanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal UniversityDepartment of Pharmacy, Shanghai Pudong Hospital, Fudan University Pudong Medical CenterAbstract Background In hospitalized patients, inadequate antibiotic dosage leading to bacterial resistance and increased antimicrobial use intensity due to overexposure to antibiotics are common problems. In the present study, we constructed a machine learning model based on patients’ clinical information to predict the clinical effectiveness of Piperacillin-tazobactam (TZP) (4:1) in treating bacterial lower respiratory tract infections (LRTIs), to assist clinicians in making better clinical decisions. Methods We collected data from patients diagnosed with LRTIs or equivalent diagnoses admitted to the Department of Pulmonary and Critical Care Medicine at Shanghai Pudong Hospital, Shanghai, between January 1, 2021, and July 31, 2023. A total of 26 relevant clinical features were extracted from this cohort. Following data preprocessing, we trained four models: Logistic Regression, Random Forest, Support Vector Machine, and Gaussian Naive Bayes. The dataset was split into training and test sets using a 7:3 ratio. The top-performing models, as determined by Receiver Operating Characteristic (ROC)-Area Under the Curve (AUC) on the independent test set, were subsequently ensembled. Ensemble model (EL) performance was evaluated using bootstrap resampling on the training set and ROC-AUC, recall, accuracy, precision, F1-score, and log loss on an independent test set. The optimal model was then deployed as a web application for clinical outcome prediction. Results A total of 1,314 patients primarily treated with TZP as initial empiric antibiotic therapy were enrolled in the analysis. The success group comprised 995 patients (75.7%), while the failure group consisted of 319 patients (24.3%). We constructed an ensemble learning model based on the Logistic Regression, Support Vector Machine and Random Forest models, which showed better overall performance. The EL model demonstrated robust performance on an independent test set, exhibiting a ROC-AUC of 0.69, a recall of 0.69, an accuracy of 0.64, a precision of 0.40, a F1-score of 0.50, and a log loss of 0.66. A corresponding web application was then developed and made available at http://106.12.146.54:1020/ . Conclusions In this study, we successfully developed and validated an EL model that effectively predicts the clinical effectiveness of TZP (4:1) in treating bacterial LRTIs. The model achieved a balanced performance across key evaluation metrics, demonstrating the model’s potential utility in clinical decision-making. The web-based application makes this model readily accessible to clinicians, potentially helping optimize antibiotic dosing decisions and reduce both inadequate treatment and overexposure. While promising, future studies with larger datasets and prospective validation are needed to further improve the model’s performance and validate its clinical utility. This work represents a step forward in using machine learning to support antimicrobial stewardship and personalized antibiotic therapy.https://doi.org/10.1186/s12890-025-03580-6Machine learningPiperacillin-tazobactamLower respiratory tract infectionsClinical dataWeb app |
| spellingShingle | Yemeng Yang Kun Han Jiatao Li Tao Zhang Zhijing Zhu Ling Su Zhaoyong Han Chunyan Xu Yi Lu Likun Pan Tao Yang A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections BMC Pulmonary Medicine Machine learning Piperacillin-tazobactam Lower respiratory tract infections Clinical data Web app |
| title | A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections |
| title_full | A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections |
| title_fullStr | A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections |
| title_full_unstemmed | A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections |
| title_short | A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections |
| title_sort | clinical data driven machine learning approach for predicting the effectiveness of piperacillin tazobactam in treating lower respiratory tract infections |
| topic | Machine learning Piperacillin-tazobactam Lower respiratory tract infections Clinical data Web app |
| url | https://doi.org/10.1186/s12890-025-03580-6 |
| work_keys_str_mv | AT yemengyang aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT kunhan aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT jiataoli aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT taozhang aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT zhijingzhu aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT lingsu aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT zhaoyonghan aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT chunyanxu aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT yilu aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT likunpan aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT taoyang aclinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT yemengyang clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT kunhan clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT jiataoli clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT taozhang clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT zhijingzhu clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT lingsu clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT zhaoyonghan clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT chunyanxu clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT yilu clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT likunpan clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections AT taoyang clinicaldatadrivenmachinelearningapproachforpredictingtheeffectivenessofpiperacillintazobactamintreatinglowerrespiratorytractinfections |