State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning
Length of stay (LOS) is a key indicator of healthcare quality and reflects the burden on the healthcare system. However, limited studies have used machine learning to predict LOS in asthma. This study aimed to explore the characteristics and associations between asthma-related admission data variabl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9890 |
| _version_ | 1797480642823323648 |
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| author | Widana Kankanamge Darsha Jayamini Farhaan Mirza M. Asif Naeem Amy Hai Yan Chan |
| author_facet | Widana Kankanamge Darsha Jayamini Farhaan Mirza M. Asif Naeem Amy Hai Yan Chan |
| author_sort | Widana Kankanamge Darsha Jayamini |
| collection | DOAJ |
| description | Length of stay (LOS) is a key indicator of healthcare quality and reflects the burden on the healthcare system. However, limited studies have used machine learning to predict LOS in asthma. This study aimed to explore the characteristics and associations between asthma-related admission data variables with LOS and to use those factors to predict LOS. A dataset of asthma-related admissions in the Auckland region was analysed using different statistical techniques. Using those predictors, machine learning models were built to predict LOS. Demographic, diagnostic, and temporal factors were associated with LOS. Māori females had the highest average LOS among all the admissions at 2.8 days. The random forest algorithm performed well, with an RMSE of 2.48, MAE of 1.67, and MSE of 6.15. The mean predicted LOS by random forest was 2.6 days with a standard deviation of 1.0. The other three algorithms were also acceptable in predicting LOS. Implementing more robust machine learning classifiers, such as artificial neural networks, could outperform the models used in this study. Future work to further develop these models with other regions and to identify the reasons behind the shorter and longer stays for asthma patients is warranted. |
| first_indexed | 2024-03-09T22:03:00Z |
| format | Article |
| id | doaj.art-df6fb9657a254769ab20e0c32af3de84 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:03:00Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-df6fb9657a254769ab20e0c32af3de842023-11-23T19:47:23ZengMDPI AGApplied Sciences2076-34172022-10-011219989010.3390/app12199890State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine LearningWidana Kankanamge Darsha Jayamini0Farhaan Mirza1M. Asif Naeem2Amy Hai Yan Chan3School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandSchool of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New ZealandSchool of Computing, National University of Computer and Emerging Sciences (NUCES), Islamabad 44000, PakistanSchool of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1010, New ZealandLength of stay (LOS) is a key indicator of healthcare quality and reflects the burden on the healthcare system. However, limited studies have used machine learning to predict LOS in asthma. This study aimed to explore the characteristics and associations between asthma-related admission data variables with LOS and to use those factors to predict LOS. A dataset of asthma-related admissions in the Auckland region was analysed using different statistical techniques. Using those predictors, machine learning models were built to predict LOS. Demographic, diagnostic, and temporal factors were associated with LOS. Māori females had the highest average LOS among all the admissions at 2.8 days. The random forest algorithm performed well, with an RMSE of 2.48, MAE of 1.67, and MSE of 6.15. The mean predicted LOS by random forest was 2.6 days with a standard deviation of 1.0. The other three algorithms were also acceptable in predicting LOS. Implementing more robust machine learning classifiers, such as artificial neural networks, could outperform the models used in this study. Future work to further develop these models with other regions and to identify the reasons behind the shorter and longer stays for asthma patients is warranted.https://www.mdpi.com/2076-3417/12/19/9890asthmalength of staymachine-learningprediction |
| spellingShingle | Widana Kankanamge Darsha Jayamini Farhaan Mirza M. Asif Naeem Amy Hai Yan Chan State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning Applied Sciences asthma length of stay machine-learning prediction |
| title | State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning |
| title_full | State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning |
| title_fullStr | State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning |
| title_full_unstemmed | State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning |
| title_short | State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning |
| title_sort | state of asthma related hospital admissions in new zealand and predicting length of stay using machine learning |
| topic | asthma length of stay machine-learning prediction |
| url | https://www.mdpi.com/2076-3417/12/19/9890 |
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