A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm
Abstract. Background:. Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine le...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer
2024-03-01
|
| Series: | Chinese Medical Journal |
| Online Access: | http://journals.lww.com/10.1097/CM9.0000000000002837 |
| _version_ | 1827314485625356288 |
|---|---|
| author | Linfeng Xi Han Kang Mei Deng Wenqing Xu Feiya Xu Qian Gao Wanmu Xie Rongguo Zhang Min Liu Zhenguo Zhai Chen Wang Xiangxiang Pan Peifang Wei |
| author_facet | Linfeng Xi Han Kang Mei Deng Wenqing Xu Feiya Xu Qian Gao Wanmu Xie Rongguo Zhang Min Liu Zhenguo Zhai Chen Wang Xiangxiang Pan Peifang Wei |
| author_sort | Linfeng Xi |
| collection | DOAJ |
| description | Abstract.
Background:. Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.
Methods:. This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Naïve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.
Results:. The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score (P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.
Conclusions:. Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE. |
| first_indexed | 2024-04-24T22:35:55Z |
| format | Article |
| id | doaj.art-8d75eb00b9c24e2c88411d788551cc22 |
| institution | Directory Open Access Journal |
| issn | 0366-6999 2542-5641 |
| language | English |
| last_indexed | 2024-04-24T22:35:55Z |
| publishDate | 2024-03-01 |
| publisher | Wolters Kluwer |
| record_format | Article |
| series | Chinese Medical Journal |
| spelling | doaj.art-8d75eb00b9c24e2c88411d788551cc222024-03-19T10:28:51ZengWolters KluwerChinese Medical Journal0366-69992542-56412024-03-01137667668210.1097/CM9.0000000000002837202403200-00008A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithmLinfeng Xi0Han Kang1Mei Deng2Wenqing Xu3Feiya Xu4Qian Gao5Wanmu Xie6Rongguo Zhang7Min Liu8Zhenguo Zhai9Chen Wang10Xiangxiang PanPeifang Wei1 Capital Medical University, Beijing 100069, China3 Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing 100025, China4 Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China6 Department of Radiology, Peking University China-Japan Friendship School of Clinical Medicine, Beijing 100191, China.1 Capital Medical University, Beijing 100069, China2 National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China2 National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China3 Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing 100025, China4 Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China2 National Center for Respiratory Medicine; State Key Laboratory of Respiratory Health and Multimorbidity; National Clinical Research Center for Respiratory Diseases; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing 100029, China1 Capital Medical University, Beijing 100069, ChinaAbstract. Background:. Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models. Methods:. This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Naïve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis. Results:. The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score (P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. Conclusions:. Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.http://journals.lww.com/10.1097/CM9.0000000000002837 |
| spellingShingle | Linfeng Xi Han Kang Mei Deng Wenqing Xu Feiya Xu Qian Gao Wanmu Xie Rongguo Zhang Min Liu Zhenguo Zhai Chen Wang Xiangxiang Pan Peifang Wei A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm Chinese Medical Journal |
| title | A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm |
| title_full | A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm |
| title_fullStr | A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm |
| title_full_unstemmed | A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm |
| title_short | A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score, revised Geneva score, and Years algorithm |
| title_sort | machine learning model for diagnosing acute pulmonary embolism and comparison with wells score revised geneva score and years algorithm |
| url | http://journals.lww.com/10.1097/CM9.0000000000002837 |
| work_keys_str_mv | AT linfengxi amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT hankang amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT meideng amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT wenqingxu amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT feiyaxu amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT qiangao amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT wanmuxie amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT rongguozhang amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT minliu amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT zhenguozhai amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT chenwang amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT xiangxiangpan amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT peifangwei amachinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT linfengxi machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT hankang machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT meideng machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT wenqingxu machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT feiyaxu machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT qiangao machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT wanmuxie machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT rongguozhang machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT minliu machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT zhenguozhai machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT chenwang machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT xiangxiangpan machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm AT peifangwei machinelearningmodelfordiagnosingacutepulmonaryembolismandcomparisonwithwellsscorerevisedgenevascoreandyearsalgorithm |