Large scale prediction of sick leave duration with nonlinear survival analysis algorithms. (15th July 2022)

• Record Type: Journal Article
• Authors: Béjar, Javier; Pérez, Raquel; Vilalta, Armand; Álvarez-Napagao, Sergio; Garcia-Gasulla, Dario
• Is Part Of: Expert systems with applications. Volume 198(2022)
• Publication Date: 2022-07-15
• Subjects: Sick leave prediction -- Survival analysis -- Machine learning -- Decision support systems -- AI in medicine; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116760
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The management of sick leaves is a critical task that public and private health systems carry out. This enables the good care of sick workers and guarantees a safe return to their jobs. Most health systems enforce regulations that establish the duration of sick leave according to general rules for groups of diagnoses. However, these regulations do not account for the particularities of workers. On the one hand, an early return to work is sometimes possible, but this does not happen unless the worker pro-actively requests it. On the other hand, the worker's health condition could demand for one or more leave extensions, but the system requires mandatory and sometimes unnecessary follow-ups, adding nuisance to patients and overhead to health systems. In both cases, the lack and excess of action by the health system represents extra costs for society. This paper proposes the analysis of a voluminous historical dataset of sick leaves (including medical and personal data) to predict the duration of future sick leaves for patients. The data mining process is performed for a large number of diagnoses to assess the possibility of using data driven models for broad decision-making. The nature and characteristics of the data makes it difficult to obtain models using classical methods, which is why the analysis focuses on non-linear machine learning-based survival analysis methods. In sight of the models performance, we move forward to its practical implementation, proposing a tool to manage the decision of what patients should be contacted at a given date using the predictions of the trained models. This tool will manage the whole cycle, continuously training on new data, performing daily predictions, and presenting the results to the health-care decision-maker for their assessment. Highlights: Modelling of Patient sick leaves at large scale from historical data. Studying non-linear survival analysis methods for decision support systems. Analysing attribute relevance extracted from decision trees survival models. Building a Decision Support System for sick leaves management.