{"id":3446,"date":"2026-06-30T12:00:00","date_gmt":"2026-06-30T12:00:00","guid":{"rendered":"https:\/\/cicds.umfcd.ro\/?post_type=lucrari_cercetari&p=3446"},"modified":"2026-08-15T00:02:17","modified_gmt":"2026-08-15T00:02:17","slug":"anticipating-the-economic-resilience-of-dental-practices-in-crises-a-machine-learning-approach","status":"publish","type":"lucrari_cercetari","link":"https:\/\/cicds.umfcd.ro\/en\/lucrari_cercetari\/anticipating-the-economic-resilience-of-dental-practices-in-crises-a-machine-learning-approach\/","title":{"rendered":"Anticipating the economic resilience of dental practices in crises: a machine learning approach"},"content":{"rendered":"
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Anticipating the economic resilience of dental practices in crises: a machine learning approach<\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\t\tRadoi, D.; Curavale, D.; Pituru, S.M.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\tFrontiers in Public Health<\/em> 2026, 14, 1858605.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\t\tAbstract\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\tBackground\/Objectives: The COVID-19 pandemic exposed the need for economic resilience in dental practices, which faced reduced patient numbers, increased costs, and service restrictions. This study aimed to develop a predictive model for dental practice resilience and to identify the financial characteristics associated with better performance during and after a crisis. Methods: We analyzed official financial statements for 2,474 Bucharest dental practices. After applying mode-specific inclusion criteria, the final analytic cohorts comprised 850 firms for crisis-year prediction in 2020 and 801 firms for the three 2021 recovery scenarios. The Dental Practice Resilience Index (DPRI) was developed as a composite measure of profitability, expense efficiency, debt-to-turnover, turnover per employee, and profit per employee. Seven regression model families were evaluated across four prediction scenarios, and continuous predictions were also translated into tertile-based risk classes for decision support. Results: Crisis-year prediction showed moderate but useful accuracy, with Linear Regression narrowly achieving the highest cross-validated performance (R\u00b2 = 0.537 \u00b1 0.078). Predicting 2021 recovery from pre-crisis data alone was more difficult, and Random Forest narrowly ranked first (R\u00b2 = 0.452 \u00b1 0.084). The strongest scenario-level results were obtained when observed 2020 data were included, with Elastic Net reaching R\u00b2 = 0.643 \u00b1 0.071 and Random Forest reaching balanced accuracy = 0.648 \u00b1 0.044. When observed crisis-year data were unavailable, a two-step synthetic recovery approach recovered part of that information, but remained below the crisis-informed configuration. Conclusions: Machine learning methods proved effective not only in predicting dental practice resilience during a crisis, but also in anticipating post-crisis recovery potential. The DPRI provided an interpretable resilience measure, while the combination of continuous prediction and tertile-based classification offered both nuanced forecasts and practical risk stratification. Observed crisis-year information substantially improved recovery forecasting, although pre-crisis indicators alone still retained meaningful predictive value.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Keywords: <\/strong><\/p>

composite indicator; COVID-19; crisis forecasting; dental practices; DPRI; financial resilience; machine learning<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\tRead the paper online<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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