Explainability analysis in predictive models based on machine learning techniques on the risk of hospital readmissions

Jencks S, Williams N, Coleman E. Rehospitalizations among patients in the Medicare fee-for-service. N Engl J Med. 2009;360:1418–28.

Article  Google Scholar 

Kansagara D. Risk prediction models for hospital readmission, a systematic review. JAMA. 2011;306(15):1688–98.

Article  Google Scholar 

Insight D. 56% of hospitals lack big data governance. Analytics plans, health IT analytics [Online]. 2017. Available https://healthitanalytics.com/news/56-of-hospitals-lack-big-data-governance-analytics-plans.

Jaana J. The diabetes risk score: A practical tool to predict type 2 diabetes risk. Expert Syst Appl. 2003;26(3):725–31.

Google Scholar 

Ortiz M, Altamar Z, Martínez C, Petrillo A, Jiménez G, García A, Medina A. Predicting 15-day unplanned readmissions in hospitalization departments: an application of logistic regression. Ingeniare Revista Chilena de Ingeniería. 2021;29(2):378–98.

Michailidis P, Dimitriadou A, Papadimitriou T, Gogas P. Forecasting hospital readmissions with machine learning. Healthcare. 2022;10:981.

Zhang D, Lee J. Effective hospital readmission prediction models using machine-learned features. BMC Health Serv Res. 2022;22:1415.

Arkaitz G. Predictive models for hospital readmission risk: A systematic review of methods. Comput Methods Programs Biomed. 2018;164:49–64.

Article  Google Scholar 

Hoyos W, Aguilar J, Toro M. Dengue models based on machine learning techniques: A systematic literature review. Artif Intell Med. 2021;119:102157. https://doi.org/10.1016/j.artmed.2021.102157.

Article  Google Scholar 

Quintero Y, Ardila D, Camargo E, Rivas F, Aguila J. Machine learning models for the prediction of the SEIRD variables for the COVID-19 pandemic based on a deep dependence analysis of variables. Comput Biol Med. 2021;134:104500. https://doi.org/10.1016/j.compbiomed.2021.104500.

Article  Google Scholar 

Camargo E, Aguilar J, Quintero Y, Rivas F, Ardila D. An incremental learning approach to prediction models of SEIRD variables in the context of the COVID-19 pandemic. Health Technol. 2022;12:867–77.

Article  Google Scholar 

Holzinger A, Langs G, Denk H, Zatloukal K, Müller H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;4(9):e1312. https://doi.org/10.1002/widm.1312.

Article  Google Scholar 

Burkart N, Huber M. A survey on the explainability of supervised machine learning. J Artif Intell Res. 2021;70:245–317.

Article  MathSciNet  MATH  Google Scholar 

Marco R, Sameer S, Carlos G. Why should i trust you? Explaining the predictions of any classifier. In: International conference on knowledge discovery and data mining. 2016.

Baig M, Hua N, Zhang E, Reece R, Spyker A, Armstrong D, Whittaker R, Robinson T, Ullah E. A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model. Med Biol Eng Comput. 2020;58:1459–66.

Article  Google Scholar 

Lo YT, Liao JC, Chen MH, Chang C, Li C. Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms. BMC Med Inform Decis Mak. 2021;21:288. https://doi.org/10.1186/s12911-021-01639-y.

Article  Google Scholar 

Ko M, Chen E, Agrawal A, Rajpurkar P, Avati A, Ng A, Basu S, Shah N. Improving hospital readmission prediction using individualized utility analysis. J Biomed Inform. 2021;119:103826. https://doi.org/10.1016/j.jbi.2021.103826.

Article  Google Scholar 

Zhao P, Yoo I, Naqvi SH. Early prediction of unplanned 30-day hospital readmission: model development and retrospective data analysis. JMIR Med Inform. 2021;23(9):e16306. https://doi.org/10.2196/16306. PMID: 33755027; PMCID: PMC8077543.

Article  Google Scholar 

Afrash M, Kazemi-Arpanahi H, Shanbehzadeh M, Nopour R, Mirbagheri E. Predicting hospital readmission risk in patients with COVID-19: a machine learning approach. Inform Med Unlocked. 2022;30:100908. https://doi.org/10.1016/j.imu.2022.100908.

Article  Google Scholar 

Shang Y, Jiang K, Wang L, Zhang Z, Zhou S, Liu Y, Dong J, Wu H. The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers. BMC Med Inform Decis Mak. 2021;21:57. https://doi.org/10.1186/s12911-021-01423-y.

Article  Google Scholar 

Huang Y, Talwar A, Chatterjee S, Aparasu R. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol. 2021;21:96. https://doi.org/10.1186/s12874-021-01284-z.

Article  Google Scholar 

Gatt M, Cassar M, Buttigieg S. A review of literature on risk prediction tools for hospital readmissions in older adults. J Health Organ Manag. 2022;36(4):521–57.

Article  Google Scholar 

Araujo M, Aguilar J, Aponte H. Fault detection system in gas lift well based on artificial immune system. In: Proc. International Joint Conference on Neural Networks, vol. 3. 2003. p. 1673–7.

Aguilar J, Jerez M, Exposito E, Villemur T. CARMiCLOC: context awareness middleware in cloud computing. In Latin American Computing Conference (CLEI). 2015

Morales L, Ouedraogo C, Aguilar J, Chassot C, Medjiah S, Drira K. Experimental comparison of the diagnostic capabilities of classification and clusteri algorithms for the QoS management in an autonomic IoT platform. SOCA. 2019;13:199–219.

Article  Google Scholar 

Sánchez M, Aguilar J, Cordero C, Valdiviezo-Díaz P, Barba-Guamán L, Chamba-Eras L. Cloud computing in smart educational environments: application in learning analytics as service. In: Rocha Á, Correia A, Adeli H, Reis L, Teixeira MM, editors. New advances in information systems and technologies. Advances in intelligent systems and computing. 2016. p. 444.

Unión Europea. Reglamento (UE) 2016/679 del Parlamento Europeo y del Consejo [Online]. Madrid; 2016. Available https://www.boe.es/doue/2016/119/L00001-00088.pdf.

Molnar C. Interpretable machine learning. A guide for making black box models explainable. Leanpub. 2019.

Ribeiro M, Singh S, Guestrin C. Model-agnostic interpretability of machine learning. Chapter 6. In: Molnar C, editor. Interpretable machine learning: a guide for making black box models explainable. Independently published. 2022.

Shearer C. The CRISP-DM model: The new blueprint for data mining. J Data Warehous. 2000;5:13–22.

Google Scholar 

Anonymous database. https://www.epssura.com/.

Breiman A. Classification and regression trees. New York; 1984.

Breiman L. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Stat Sci. 2001;16(3):199–231.

Article  MATH  Google Scholar 

Freund Y, Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119–39.

Article  MathSciNet  MATH  Google Scholar 

Ledoit O, Wolf M, Honey I. Shrunk the sample covariance matrix. J Portf Manag. 2004;30:110–9.

Article  Google Scholar 

Hoyos W, Aguilar J, Toro M. A clinical decision-support system for dengue based on fuzzy cognitive maps. Health Care Manag Sci. 2022;25:666–81.

Article  Google Scholar 

Vizcarrondo J, Aguilar J, Exposito E, Subias A. ARMISCOM: Autonomic reflective middleware for management service composition. In: Global Information Infrastructure and Networking Symposium (GIIS). 2012.

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