Time series forecasting of bed occupancy in mental health facilities in India using machine learning
Abstract Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, like bed occupancy forecasting, is crucia...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86418-9 |
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| author | G. Avinash Hariom Pachori Avinash Sharma SukhDev Mishra |
| author_facet | G. Avinash Hariom Pachori Avinash Sharma SukhDev Mishra |
| author_sort | G. Avinash |
| collection | DOAJ |
| description | Abstract Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, like bed occupancy forecasting, is crucial to ensure proper patient care and reduce the burden on healthcare facilities. This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024. Accuracy of models were evaluated using Mean Absolute Percentage Error, and Diebold–Mariano test for assessing differences in predictive performance. Further, we forecast the bed occupancy, providing crucial insights for healthcare administrators in capacity planning and resource allocation, supporting data-driven decisions and enhancing the quality of mental health services in India. |
| format | Article |
| id | doaj-art-675f327c70ea4cdb8cf6181a38425075 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-675f327c70ea4cdb8cf6181a384250752025-01-26T12:31:41ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-86418-9Time series forecasting of bed occupancy in mental health facilities in India using machine learningG. Avinash0Hariom Pachori1Avinash Sharma2SukhDev Mishra3Department of Biostatistics, Division of Health Sciences, ICMR-National Institute of Occupational HealthDepartment of Computer and Data Management, Central Institute of PsychiatryDepartment of Psychiatry, Central Institute of Psychiatry (CIP)Department of Biostatistics, Division of Health Sciences, ICMR-National Institute of Occupational HealthAbstract Machine learning models are vital for forecasting and optimizing healthcare parameters, especially in the context of rising mental health issues in India and globally. With increasing demand for mental health services, effective resource management, like bed occupancy forecasting, is crucial to ensure proper patient care and reduce the burden on healthcare facilities. This study applies six machine learning models, namely Support Vector Regression, eXtreme Gradient Boosting, Random Forest, K-Nearest Neighbors, Gradient Boosting, and Decision Tree, to forecast weekly bed occupancy of the second largest mental hospital in India, using data from 2008 to 2024. Accuracy of models were evaluated using Mean Absolute Percentage Error, and Diebold–Mariano test for assessing differences in predictive performance. Further, we forecast the bed occupancy, providing crucial insights for healthcare administrators in capacity planning and resource allocation, supporting data-driven decisions and enhancing the quality of mental health services in India.https://doi.org/10.1038/s41598-025-86418-9Bed occupancy forecastingHealthcare resource managementMental health hospitalMachine learning in healthcareTime series analysisPatient care optimization |
| spellingShingle | G. Avinash Hariom Pachori Avinash Sharma SukhDev Mishra Time series forecasting of bed occupancy in mental health facilities in India using machine learning Scientific Reports Bed occupancy forecasting Healthcare resource management Mental health hospital Machine learning in healthcare Time series analysis Patient care optimization |
| title | Time series forecasting of bed occupancy in mental health facilities in India using machine learning |
| title_full | Time series forecasting of bed occupancy in mental health facilities in India using machine learning |
| title_fullStr | Time series forecasting of bed occupancy in mental health facilities in India using machine learning |
| title_full_unstemmed | Time series forecasting of bed occupancy in mental health facilities in India using machine learning |
| title_short | Time series forecasting of bed occupancy in mental health facilities in India using machine learning |
| title_sort | time series forecasting of bed occupancy in mental health facilities in india using machine learning |
| topic | Bed occupancy forecasting Healthcare resource management Mental health hospital Machine learning in healthcare Time series analysis Patient care optimization |
| url | https://doi.org/10.1038/s41598-025-86418-9 |
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