Abstract
Hospital readmission is a crucial healthcare quality measure that helps in determining the level of quality of care that a hospital offers to a patient and has proven to be immensely expensive. It is estimated that more than $25 billion are spent yearly due to readmission of diabetic patients in the USA. This paper benchmarks existing models and proposes a new embedding-based state-of-the-art deep neural network(DNN). The model can identify whether a hospitalized diabetic patient will be readmitted within 30 days or not with an accuracy of 95.2% and Area Under the Receiver Operating Characteristics (AUROC) of 97.4% on data collected from 130 US hospitals between 1999 and 2008. The results are encouraging with patients having changes in medication while admitted having a high chance of getting readmitted. Identifying prospective patients for readmission could help the hospital systems in improving their inpatient care, thereby saving them from unnecessary expenditures.
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References
Bhuvan, M.S., et al.: Identifying diabetic patients with high risk of readmission. In: arXiv:abs/1602.04257 (2016)
Bowyer, K.W., et al.: SMOTE: synthetic minority over-sampling technique In: CoRR abs/1106.1813. arXiv:1106.1813. http://arxiv.org/abs/1106.1813 (2011)
Chopra, C., et al.: Recurrent Neural Networks with Non-Sequential Data to Predict Hospital Readmission of Diabetic Patients, pp. 18–23, Oct 2017. https://doi.org/10.1145/3155077.3155081
Damery, S., Combes, G.: Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: a retrospective cohort study. BMJ Open 7(7) (2017). issn: 2044-6055. https://doi.org/10.1136/bmjopen-2017-016921
Diabetes 130-US hospitals for years 1999–2008 data set. https://archive.ics.uci.edu/ml/datasets/diabetes+130-us+hospitals+for+years+1999-2008 (2008)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 63(1), 3–42 (2006). issn: 1573-0565. https://doi.org/10.1007/s10994-006-6226-1
Goudjerkan, T., Jayabalan, M.: Predicting 30-day hospital readmission for diabetes patients using multilayer perceptron. Int. J. Adv. Comput. Sci. Appl. 10, 268–275 (2019). https://doi.org/10.14569/IJACSA.2019.0100236
Hammoudeh, A., et al.: Predicting Hospital Readmission among Diabetics using Deep Learning (2018)
Howard, J., et al.: fastai. https://github.com/fastai/fastai (2018)
International Diabetes Federation. IDF Diabetes Atlas, 8th edn. Brussels, International Diabetes Federation, Belgium (2017)
Lin, C.Y.: What are Predictors of Medication Change and Hospital Readmission in Diabetic Patients? (2018)
Low, L., et al.: Predicting 30-Day readmissions: performance of the LACE index compared with a regression model among general medicine patients in Singapore. BioMed Research International 2015, p. 169870 (2015). https://doi.org/10.1155/2015/169870
Mander, A.: LARS: Stata Module to Perform Least Angle Regression. Statistical Software Components, Boston College Department of Economics (2006)
Mingle, D.: Predicting diabetic readmission rates: moving beyond Hba1c. Curr. Trends Biomed. Eng. Biosci. 7(3), 555707 (2015). https://doi.org/10.19080/CTBEB.2017.07.555715
Munnangi, H., Chakraborty, G.: Predicting Readmission of Diabetic Patients using the High performance Support Vector Machine Algorithm of SAS R Enterprise MinerTM (2015)
Ostling, S.: The relationship between diabetes mellitus and 30-day readmission rates. Clin. Diab. Endocrinol. 3(1), 3 (2017). https://doi.org/10.1186/s40842-016-0040-x
Pham, H.N., et al.: Predicting hospital readmission patterns of diabetic patients using ensemble model and cluster analysis. In: 2019 International Conference on System Science and Engineering (ICSSE), pp. 273–278 (2019). https://doi.org/10.1109/ICSSE.2019.8823441
Readmissions Reduction Program: https://www.cms.gov/Medicare/medicare-fee-for-service-payment/acuteinpatientPPS/readmissions-reduction-program.html
Rubin, D.J.: Correction to: Hospital readmission of patients with diabetes. Curr. Diab. Rep. 18(4), 21 (2018). issn: 1539-0829. https://doi.org/10.1007/s11892-018-0989-1
Srivastava, Nitish, et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Strack, B., et al.: Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed Research International 2014, p. 781670 (2014). https://doi.org/10.1155/2014/781670
Tibshirani, Robert: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267–288 (1994)
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Sarthak, Shukla, S., Prakash Tripathi, S. (2021). EmbPred30: Assessing 30-Days Readmission for Diabetic Patients Using Categorical Embeddings. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K., Suryani, E. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 1168. Springer, Singapore. https://doi.org/10.1007/978-981-15-5345-5_7
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