Prediction of pulmonary embolism by an explainable machine learning approach in the real world
Abstract In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence–...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-75435-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850075745670397952 |
|---|---|
| author | Qiao Zhou Ruichen Huang Xingyu Xiong Zongan Liang Wei Zhang |
| author_facet | Qiao Zhou Ruichen Huang Xingyu Xiong Zongan Liang Wei Zhang |
| author_sort | Qiao Zhou |
| collection | DOAJ |
| description | Abstract In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020. 126 features were screened and diverse MLAs were utilized to craft predictive models for PE. Area under the receiver operating characteristic curves (AUC) were used to evaluate their performance and SHapley Additive exPlanation (SHAP) values were utilized to elucidate the prediction model. Regarding the efficacy of the single model that most accurately predicted the outcome, RF demonstrated the highest efficacy in predicting outcomes, with an AUC of 0.776 (95% CI 0.774–0.778). The SHAP summary plot delineated the positive and negative effects of features attributed to the RF prediction model, including D-dimer, activated partial thromboplastin time (APTT), fibrin and fibrinogen degradation products (FFDP), platelet count, albumin, cholesterol, and sodium. Furthermore, the SHAP dependence plot illustrated the impact of individual features on the RF prediction model. Finally, the MLA based PE predicting model was designed as a web page that can be applied to the platform of clinical management. In this study, PE prediction model was successfully established and designed as a web page, facilitating the optimization of early diagnosis and timely treatment strategies to enhance PE patient outcomes. |
| format | Article |
| id | doaj-art-3f691fc27af2413b967098cafc4e2be4 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3f691fc27af2413b967098cafc4e2be42025-08-20T02:46:13ZengNature PortfolioScientific Reports2045-23222025-01-011511810.1038/s41598-024-75435-9Prediction of pulmonary embolism by an explainable machine learning approach in the real worldQiao Zhou0Ruichen Huang1Xingyu Xiong2Zongan Liang3Wei Zhang4Department of Respiratory and Critical Care Medicine, Changhai Hospital, The Second Military Medical UniversityDepartment of Respiratory and Critical Care Medicine, Changhai Hospital, The Second Military Medical UniversityDepartment of Respiratory and Critical Care Medicine, West China Hospital of Sichuan UniversityDepartment of Respiratory and Critical Care Medicine, West China Hospital of Sichuan UniversityDepartment of Respiratory and Critical Care Medicine, Changhai Hospital, The Second Military Medical UniversityAbstract In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence–based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020. 126 features were screened and diverse MLAs were utilized to craft predictive models for PE. Area under the receiver operating characteristic curves (AUC) were used to evaluate their performance and SHapley Additive exPlanation (SHAP) values were utilized to elucidate the prediction model. Regarding the efficacy of the single model that most accurately predicted the outcome, RF demonstrated the highest efficacy in predicting outcomes, with an AUC of 0.776 (95% CI 0.774–0.778). The SHAP summary plot delineated the positive and negative effects of features attributed to the RF prediction model, including D-dimer, activated partial thromboplastin time (APTT), fibrin and fibrinogen degradation products (FFDP), platelet count, albumin, cholesterol, and sodium. Furthermore, the SHAP dependence plot illustrated the impact of individual features on the RF prediction model. Finally, the MLA based PE predicting model was designed as a web page that can be applied to the platform of clinical management. In this study, PE prediction model was successfully established and designed as a web page, facilitating the optimization of early diagnosis and timely treatment strategies to enhance PE patient outcomes.https://doi.org/10.1038/s41598-024-75435-9Pulmonary embolismMachine learning algorithmsPrediction modelSHAP value |
| spellingShingle | Qiao Zhou Ruichen Huang Xingyu Xiong Zongan Liang Wei Zhang Prediction of pulmonary embolism by an explainable machine learning approach in the real world Scientific Reports Pulmonary embolism Machine learning algorithms Prediction model SHAP value |
| title | Prediction of pulmonary embolism by an explainable machine learning approach in the real world |
| title_full | Prediction of pulmonary embolism by an explainable machine learning approach in the real world |
| title_fullStr | Prediction of pulmonary embolism by an explainable machine learning approach in the real world |
| title_full_unstemmed | Prediction of pulmonary embolism by an explainable machine learning approach in the real world |
| title_short | Prediction of pulmonary embolism by an explainable machine learning approach in the real world |
| title_sort | prediction of pulmonary embolism by an explainable machine learning approach in the real world |
| topic | Pulmonary embolism Machine learning algorithms Prediction model SHAP value |
| url | https://doi.org/10.1038/s41598-024-75435-9 |
| work_keys_str_mv | AT qiaozhou predictionofpulmonaryembolismbyanexplainablemachinelearningapproachintherealworld AT ruichenhuang predictionofpulmonaryembolismbyanexplainablemachinelearningapproachintherealworld AT xingyuxiong predictionofpulmonaryembolismbyanexplainablemachinelearningapproachintherealworld AT zonganliang predictionofpulmonaryembolismbyanexplainablemachinelearningapproachintherealworld AT weizhang predictionofpulmonaryembolismbyanexplainablemachinelearningapproachintherealworld |