Machine Learning and Statistical Insights into Hospital Stay Durations: The Italian EHR Case
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909594353139712 |
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| author | Andric, Marina Dragoni, Mauro |
| author_facet | Andric, Marina Dragoni, Mauro |
| contents | Length of hospital stay is a critical metric for assessing healthcare quality and optimizing hospital resource management. This study aims to identify factors influencing LoS within the Italian healthcare context, using a dataset of hospitalization records from over 60 healthcare facilities in the Piedmont region, spanning from 2020 to 2023. We explored a variety of features, including patient characteristics, comorbidities, admission details, and hospital-specific factors. Significant correlations were found between LoS and features such as age group, comorbidity score, admission type, and the month of admission. Machine learning models, specifically CatBoost and Random Forest, were used to predict LoS. The highest R2 score, 0.49, was achieved with CatBoost, demonstrating good predictive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning and Statistical Insights into Hospital Stay Durations: The Italian EHR Case Andric, Marina Dragoni, Mauro Machine Learning Length of hospital stay is a critical metric for assessing healthcare quality and optimizing hospital resource management. This study aims to identify factors influencing LoS within the Italian healthcare context, using a dataset of hospitalization records from over 60 healthcare facilities in the Piedmont region, spanning from 2020 to 2023. We explored a variety of features, including patient characteristics, comorbidities, admission details, and hospital-specific factors. Significant correlations were found between LoS and features such as age group, comorbidity score, admission type, and the month of admission. Machine learning models, specifically CatBoost and Random Forest, were used to predict LoS. The highest R2 score, 0.49, was achieved with CatBoost, demonstrating good predictive performance. |
| title | Machine Learning and Statistical Insights into Hospital Stay Durations: The Italian EHR Case |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.18393 |