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Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk

Author

Listed:
  • Javier-Leonardo Gonzalez-Rodriguez
  • Carlos Franco
  • Olga Pinzón-Espitia
  • Vicent Caballer
  • Edgar Alfonso-Lizarazo
  • Vincent Augusto

Abstract

Objective: To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the analysis of comorbidities. Materials and methods: In this cross-sectional study, we have used data from 11,028 anonymized records of patients admitted to a high-complexity hospital in Bogota, Colombia between 2017–2019 with a primary diagnosis of Diabetes. These cases were classified according to Charlson’s comorbidity index in several risk categories. The main variables analyzed in this study are hospitalization costs (which include pharmaceutical and non-pharmaceutical expenditures), age, gender, length of stay, medicines and services consumed, and comorbidities assessed by the Charlson’s index. The model’s dependent variable is expenditure (composed of pharmaceutical and non-pharmaceutical expenditures). Based on these variables, different machine learning models (Multivariate linear regression, Lasso model, and Neural Networks) were used to estimate the pharmaceutical and non-pharmaceutical expenditures associated with the clinical risk classification. To evaluate the performance of these models, different metrics were used: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Results: The results indicate that the Neural Networks model performed better in terms of accuracy in predicting pharmaceutical and non-pharmaceutical expenditures considering the clinical risk based on Charlson’s comorbidity index. A deeper understanding and experimentation with Neural Networks can improve these preliminary results, therefore we can also conclude that the main variables used and those that were proposed can be used as predictors for the medical expenditures of patients with diabetes type-II. Conclusions: With the increase of technology elements and tools, it is possible to build models that allow decision-makers in hospitals to improve the resource planning process given the accuracy obtained with the different models tested.

Suggested Citation

  • Javier-Leonardo Gonzalez-Rodriguez & Carlos Franco & Olga Pinzón-Espitia & Vicent Caballer & Edgar Alfonso-Lizarazo & Vincent Augusto, 2024. "Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-17, June.
    • Handle: RePEc:plo:pone00:0301860
    DOI: 10.1371/journal.pone.0301860
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    References listed on IDEAS

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    1. Reid, R.J. & MacWilliam, l. & Verhulst, L. & Roos, N. & Atkinson, M., 2001. "Performance of the ACG Case-Mix System in Two Canadian Provinces," Centre for Health Services and Policy Research 2001:1r, University of British Columbia - Centre for Health Services and Policy Research..
    2. Caballer-Tarazona, Vicent & Guadalajara-Olmeda, Natividad & Vivas-Consuelo, David, 2019. "Predicting healthcare expenditure by multimorbidity groups," Health Policy, Elsevier, vol. 123(4), pages 427-434.
    3. Vivas-Consuelo, David & Usó-Talamantes, Ruth & Trillo-Mata, José Luis & Caballer-Tarazona, Maria & Barrachina-Martínez, Isabel & Buigues-Pastor, Laia, 2014. "Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups," Health Policy, Elsevier, vol. 116(2), pages 188-195.
    4. Shi, Xunpeng & Wang, Keying & Cheong, Tsun Se & Zhang, Hongwu, 2020. "Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data," Energy Economics, Elsevier, vol. 92(C).
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