Application of machine learning models on predicting the length of hospital stay in fragility fracture patients
Abstract Background The rate of geriatric hip fracture in Hong Kong is increasing steadily and associated mortality in fragility fracture is high. Moreover, fragility fracture patients increase the pressure on hospital bed demand. Hence, this study aims to develop a predictive model on the length of...
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-024-02417-2 |
| _version_ | 1797274411100798976 |
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| author | Chun-Hei Lai Prudence Kwan-Lam Mok Wai-Wang Chau Sheung-Wai Law |
| author_facet | Chun-Hei Lai Prudence Kwan-Lam Mok Wai-Wang Chau Sheung-Wai Law |
| author_sort | Chun-Hei Lai |
| collection | DOAJ |
| description | Abstract Background The rate of geriatric hip fracture in Hong Kong is increasing steadily and associated mortality in fragility fracture is high. Moreover, fragility fracture patients increase the pressure on hospital bed demand. Hence, this study aims to develop a predictive model on the length of hospital stay (LOS) of geriatric fragility fracture patients using machine learning (ML) techniques. Methods In this study, we use the basic information, such as gender, age, residence type, etc., and medical parameters of patients, such as the modified functional ambulation classification score (MFAC), elderly mobility scale (EMS), modified Barthel index (MBI) etc, to predict whether the length of stay would exceed 21 days or not. Results Our results are promising despite the relatively small sample size of 8000 data. We develop various models with three approaches, namely (1) regularizing gradient boosting frameworks, (2) custom-built artificial neural network and (3) Google’s Wide & Deep Learning technique. Our best results resulted from our Wide & Deep model with an accuracy of 0.79, with a precision of 0.73, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84. Feature importance analysis indicates (1) the type of hospital the patient is admitted to, (2) the mental state of the patient and (3) the length of stay at the acute hospital all have a relatively strong impact on the length of stay at palliative care. Conclusions Applying ML techniques to improve the quality and efficiency in the healthcare sector is becoming popular in Hong Kong and around the globe, but there has not yet been research related to fragility fracture. The integration of machine learning may be useful for health-care professionals to better identify fragility fracture patients at risk of prolonged hospital stays. These findings underline the usefulness of machine learning techniques in optimizing resource allocation by identifying high risk individuals and providing appropriate management to improve treatment outcome. |
| first_indexed | 2024-03-07T14:57:56Z |
| format | Article |
| id | doaj.art-e7c5f63fb02c42de8332b3facd2df078 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-07T14:57:56Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-e7c5f63fb02c42de8332b3facd2df0782024-03-05T19:19:57ZengBMCBMC Medical Informatics and Decision Making1472-69472024-01-0124111910.1186/s12911-024-02417-2Application of machine learning models on predicting the length of hospital stay in fragility fracture patientsChun-Hei Lai0Prudence Kwan-Lam Mok1Wai-Wang Chau2Sheung-Wai Law3Department of Orthopaedics and Traumatology, Chinese University of Hong Kong, Prince of Wales HospitalDepartment of Orthopaedics and Traumatology, Chinese University of Hong Kong, Prince of Wales HospitalDepartment of Orthopaedics and Traumatology, Chinese University of Hong Kong, Prince of Wales HospitalDepartment of Orthopaedics and Traumatology, Chinese University of Hong Kong, Prince of Wales HospitalAbstract Background The rate of geriatric hip fracture in Hong Kong is increasing steadily and associated mortality in fragility fracture is high. Moreover, fragility fracture patients increase the pressure on hospital bed demand. Hence, this study aims to develop a predictive model on the length of hospital stay (LOS) of geriatric fragility fracture patients using machine learning (ML) techniques. Methods In this study, we use the basic information, such as gender, age, residence type, etc., and medical parameters of patients, such as the modified functional ambulation classification score (MFAC), elderly mobility scale (EMS), modified Barthel index (MBI) etc, to predict whether the length of stay would exceed 21 days or not. Results Our results are promising despite the relatively small sample size of 8000 data. We develop various models with three approaches, namely (1) regularizing gradient boosting frameworks, (2) custom-built artificial neural network and (3) Google’s Wide & Deep Learning technique. Our best results resulted from our Wide & Deep model with an accuracy of 0.79, with a precision of 0.73, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84. Feature importance analysis indicates (1) the type of hospital the patient is admitted to, (2) the mental state of the patient and (3) the length of stay at the acute hospital all have a relatively strong impact on the length of stay at palliative care. Conclusions Applying ML techniques to improve the quality and efficiency in the healthcare sector is becoming popular in Hong Kong and around the globe, but there has not yet been research related to fragility fracture. The integration of machine learning may be useful for health-care professionals to better identify fragility fracture patients at risk of prolonged hospital stays. These findings underline the usefulness of machine learning techniques in optimizing resource allocation by identifying high risk individuals and providing appropriate management to improve treatment outcome.https://doi.org/10.1186/s12911-024-02417-2Machine learningFragility fracturePredictive medicineLength of stayGeriatric hip fracture |
| spellingShingle | Chun-Hei Lai Prudence Kwan-Lam Mok Wai-Wang Chau Sheung-Wai Law Application of machine learning models on predicting the length of hospital stay in fragility fracture patients BMC Medical Informatics and Decision Making Machine learning Fragility fracture Predictive medicine Length of stay Geriatric hip fracture |
| title | Application of machine learning models on predicting the length of hospital stay in fragility fracture patients |
| title_full | Application of machine learning models on predicting the length of hospital stay in fragility fracture patients |
| title_fullStr | Application of machine learning models on predicting the length of hospital stay in fragility fracture patients |
| title_full_unstemmed | Application of machine learning models on predicting the length of hospital stay in fragility fracture patients |
| title_short | Application of machine learning models on predicting the length of hospital stay in fragility fracture patients |
| title_sort | application of machine learning models on predicting the length of hospital stay in fragility fracture patients |
| topic | Machine learning Fragility fracture Predictive medicine Length of stay Geriatric hip fracture |
| url | https://doi.org/10.1186/s12911-024-02417-2 |
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