Version 1
: Received: 29 March 2024 / Approved: 29 March 2024 / Online: 1 April 2024 (10:07:07 CEST)
How to cite:
Santamato, V.; Tricase, C.; Faccilongo, N.; Iacoviello, M.; Pange, J.; Marengo, A. Machine Learning for Evaluating Hospital Mobility: An Italian Case Study. Preprints2024, 2024040006. https://doi.org/10.20944/preprints202404.0006.v1
Santamato, V.; Tricase, C.; Faccilongo, N.; Iacoviello, M.; Pange, J.; Marengo, A. Machine Learning for Evaluating Hospital Mobility: An Italian Case Study. Preprints 2024, 2024040006. https://doi.org/10.20944/preprints202404.0006.v1
Santamato, V.; Tricase, C.; Faccilongo, N.; Iacoviello, M.; Pange, J.; Marengo, A. Machine Learning for Evaluating Hospital Mobility: An Italian Case Study. Preprints2024, 2024040006. https://doi.org/10.20944/preprints202404.0006.v1
APA Style
Santamato, V., Tricase, C., Faccilongo, N., Iacoviello, M., Pange, J., & Marengo, A. (2024). Machine Learning for Evaluating Hospital Mobility: An Italian Case Study. Preprints. https://doi.org/10.20944/preprints202404.0006.v1
Chicago/Turabian Style
Santamato, V., Jenny Pange and Agostino Marengo. 2024 "Machine Learning for Evaluating Hospital Mobility: An Italian Case Study" Preprints. https://doi.org/10.20944/preprints202404.0006.v1
Abstract
This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
