ArticleBMC medical informatics and decision making2024
Application of machine learning models on predicting the length of hospital stay in fragility fracture patients.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Machine learning models incorporating somatic and mental comorbidities for prolonged length-of-stay prediction in a maximum care university hospital.BMC medical informatics and decision making · 2025Article
- Can general purpose large language models assist pediatricians in predicting infants with serious bacterial infection?BMC medical informatics and decision making · 2025Article
- Prehospital factors of survival to hospital admission in blunt traumatic out-of-hospital cardiac arrest: a nationwide 11-year study.Resuscitation plus · 2025Article
- Quality assurance in machine Learning: Do we hit the mark for total joint arthroplasty models? A systematic review.Journal of clinical orthopaedics and trauma · 2025Review
- Artificial intelligence-based approaches for advance care planning: a scoping review.BMC palliative care · 2025Article
- The power of artificial intelligence for managing pandemics: A primer for public health professionals.The International journal of health planning and management · 2025Review
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4 authors.
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Abstract
backgroundThe 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.
methodsIn 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.
resultsOur 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.
conclusionsApplying 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.
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