Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example
Abstract Background There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with spontaneous supratentorial intracerebral he...
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| Format: | Article |
| Language: | English |
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BMC
2022-01-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-022-01752-6 |
| _version_ | 1828138468959584256 |
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| author | Huimin Wang Jianxiang Tang Mengyao Wu Xiaoyu Wang Tao Zhang |
| author_facet | Huimin Wang Jianxiang Tang Mengyao Wu Xiaoyu Wang Tao Zhang |
| author_sort | Huimin Wang |
| collection | DOAJ |
| description | Abstract Background There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example, this study adopted the missing data processing evaluation criteria more suitable for clinical decision making, aiming at systematically exploring the performance and applicability of single machine learning algorithms and ensemble learning (EL) under different data missing scenarios, as well as whether they had more advantages than traditional methods, so as to provide basis and reference for the selection of suitable missing data processing method in practical clinical decision making. Methods The whole process consisted of four main steps: (1) Based on the original complete data set, missing data was generated by simulation under different missing scenarios (missing mechanisms, missing proportions and ratios of missing proportions of each group). (2) Machine learning and traditional methods (eight methods in total) were applied to impute missing values. (3) The performances of imputation techniques were evaluated and compared by estimating the sensitivity, AUC and Kappa values of prediction models. (4) Statistical tests were used to evaluate whether the observed performance differences were statistically significant. Results The performances of missing data processing methods were different to a certain extent in different missing scenarios. On the whole, machine learning had better imputation performance than traditional methods, especially in scenarios with high missing proportions. Compared with single machine learning algorithms, the performance of EL was more prominent, followed by neural networks. Meanwhile, EL was most suitable for missing imputation under MAR (the ratio of missing proportion 2:1) mechanism, and its average sensitivity, AUC and Kappa values reached 0.908, 0.924 and 0.596 respectively. Conclusions In clinical decision making, the characteristics of missing data should be actively explored before formulating missing data processing strategies. The outstanding imputation performance of machine learning methods, especially EL, shed light on the development of missing data processing technology, and provided methodological support for clinical decision making in presence of incomplete data. |
| first_indexed | 2024-04-11T18:36:14Z |
| format | Article |
| id | doaj.art-32894b29e9764b2f99a955b05bac86f1 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-11T18:36:14Z |
| publishDate | 2022-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-32894b29e9764b2f99a955b05bac86f12022-12-22T04:09:14ZengBMCBMC Medical Informatics and Decision Making1472-69472022-01-0122111410.1186/s12911-022-01752-6Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an exampleHuimin Wang0Jianxiang Tang1Mengyao Wu2Xiaoyu Wang3Tao Zhang4Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan UniversityDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan UniversityDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan UniversityDepartment of Neurosurgery, West China Hospital, Sichuan UniversityDepartment of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan UniversityAbstract Background There are often many missing values in medical data, which directly affect the accuracy of clinical decision making. Discharge assessment is an important part of clinical decision making. Taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example, this study adopted the missing data processing evaluation criteria more suitable for clinical decision making, aiming at systematically exploring the performance and applicability of single machine learning algorithms and ensemble learning (EL) under different data missing scenarios, as well as whether they had more advantages than traditional methods, so as to provide basis and reference for the selection of suitable missing data processing method in practical clinical decision making. Methods The whole process consisted of four main steps: (1) Based on the original complete data set, missing data was generated by simulation under different missing scenarios (missing mechanisms, missing proportions and ratios of missing proportions of each group). (2) Machine learning and traditional methods (eight methods in total) were applied to impute missing values. (3) The performances of imputation techniques were evaluated and compared by estimating the sensitivity, AUC and Kappa values of prediction models. (4) Statistical tests were used to evaluate whether the observed performance differences were statistically significant. Results The performances of missing data processing methods were different to a certain extent in different missing scenarios. On the whole, machine learning had better imputation performance than traditional methods, especially in scenarios with high missing proportions. Compared with single machine learning algorithms, the performance of EL was more prominent, followed by neural networks. Meanwhile, EL was most suitable for missing imputation under MAR (the ratio of missing proportion 2:1) mechanism, and its average sensitivity, AUC and Kappa values reached 0.908, 0.924 and 0.596 respectively. Conclusions In clinical decision making, the characteristics of missing data should be actively explored before formulating missing data processing strategies. The outstanding imputation performance of machine learning methods, especially EL, shed light on the development of missing data processing technology, and provided methodological support for clinical decision making in presence of incomplete data.https://doi.org/10.1186/s12911-022-01752-6Clinical decision makingMissing dataImputationMachine learningEnsemble learningDischarge assessment |
| spellingShingle | Huimin Wang Jianxiang Tang Mengyao Wu Xiaoyu Wang Tao Zhang Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example BMC Medical Informatics and Decision Making Clinical decision making Missing data Imputation Machine learning Ensemble learning Discharge assessment |
| title | Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example |
| title_full | Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example |
| title_fullStr | Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example |
| title_full_unstemmed | Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example |
| title_short | Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example |
| title_sort | application of machine learning missing data imputation techniques in clinical decision making taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example |
| topic | Clinical decision making Missing data Imputation Machine learning Ensemble learning Discharge assessment |
| url | https://doi.org/10.1186/s12911-022-01752-6 |
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