Abstract
In recent years, healthcare spending has risen and become a burden on governments especially in the US. The selection of the primary medical procedure by physicians is the first step in the patient treatment process and is considered to be one of the main causes for hospital readmissions if it is not done correctly. In this paper, we propose a system that can identify with high accuracy the primary medical procedure for a newly admitted patient. We propose three approaches to anticipate which medical procedure should be primary. Additionally, we propose the procedure graph, which shows all possible paths that a new patient may undertake during the course of treatment. Finally, we extract the possible associations between the primary procedure and other procedures in the same hospital visit. The results show the ability of our proposed system to identify which procedure should be primary and extract its associations with other procedures.
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References
Al-Mardini, M., Hajja, A., Clover, L., Olaleye, D., Park, Y., Paulson, J., & Xiao, Y. (2016). Reduction of Hospital Readmissions Through Clustering Based Actionable Knowledge Mining. In IEEE/WIC/ACM International Conference on Web Intelligence (WI) (pp. 444–448): IEEE.
Almardini, M., Hajja, A., Raś, Z. W., Clover, L., Olaleye, D., Park, Y., Paulson, J., & Xiao, Y. (2016). Reduction of Readmissions to Hospitals Based on Actionable Knowledge Discovery and Personalization. In Beyond Databases, Architectures and Structures. Communications in Computer and Information Science, (Vol. 613 pp. 39–55): Springer.
Arbajian, P., Hajja, A., Raś, Z.W., & Wieczorkowska, A. A. (2019). Effect of Speech Segment Samples Selection in Stutter Block Detection and Remediation. Journal of Intelligent Information Systems.
Cabitza, F., Locoro, A., & Batini, C. (2018). Making open data more personal through a social value perspective: a methodological approach. Information Systems Frontiers, 1–18.
Ciecierski, K. A., Raś, Z. W., & Przybyszewski, A. W. (2014). Foundations of automatic system for intrasurgical localization of subthalamic nucleus in parkinson patients. Web Intelligence and Agent Systems: An International Journal, 12(1), 63–82.
Coopers, P. (2006). The Price of Excess. Identifying Waste in Healthcare Spending.
Efendi, R., Samsudin, N. A., Deris, M. M., & Ting, Y. G. (2018). Flu diagnosis system using Jaccard Index and rough set approaches. In Journal of Physics: Conference Series, (Vol. 1004 p. 012014): IOP Publishing.
Erdeniz, S. P., Maglogiannis, I., Menychtas, A., Felfernig, A., & Tran, T. N. T. (2018). Recommender Systems for IoT Enabled m-Health Applications. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 227–237): Springer.
Fecher, K., McCarthy, L., Porreca, D. E., & Yaraghi, N. (2020). Assessing the benefits of integrating health information exchange services into the medical practices’ workflow. Information Systems Frontiers, 1–7.
Felfernig, A., Friedrich, G., Jannach, D., & Zanker, M. (2015). Constraint-based Recommender Systems. In Recommender Systems Handbook (pp. 161–190): Springer.
Gorman, L. (2013). Priceless: curing the healthcare crisis. Business Economics, 48(1), 81–83.
Gottlieb, A., Stein, G. Y., Ruppin, E., Altman, R. B., & Sharan, R. (2013). A method for inferring medical diagnoses from patient similarities. BMC medicine, 11(1), 194.
Healthcare Cost and Utilization Project (HCUP). (2019). Overview Of The State Inpatient Databases (SID). Accessed: Feb 26, 2019.
Hines, A. L., Barrett, M. L., Jiang, H. J., & Steiner, C. A. (2014). Conditions with the largest number of adult hospital readmissions by payer, 2011. HCUP Statistical Brief, 172.
Huang, C. D., Goo, J., Behara, R. S., & Agarwal, A. (2018). Clinical decision support system for managing copd-related readmission risk. Information Systems Frontiers, 1–13.
Jia, Z., Lu, X., Duan, H., & Li, H. (2019). Using the distance between sets of hierarchical taxonomic clinical concepts to measure patient similarity. BMC Medical Informatics and Decision Making, 19(1), 91.
Kankanhalli, A., Hahn, J., Tan, S., & Gao, G. (2016). Big data and analytics in healthcare: introduction to the special section. Information Systems Frontiers, 18(2), 233–235.
Keehan, S. P., Cuckler, G. A., Sisko, A. M., & et al. (2015). National health expenditure projections, 2014–24: Spending growth faster than recent trends. Health Affairs, 34(8), 1407–1417.
Lally, A., Bagchi, S., Barborak, M. A., Buchanan, D. W., Chu-Carroll, J., Ferrucci, D. A., Glass, M. R., Kalyanpur, A., Mueller, E. T., Murdock, J. W., Patwardhan, S., & Prager, J. M. (2017). Watsonpaths: Scenario-based Question Answering and Inference over Unstructured Information. AI Magazine, 38(2), 59.
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6, 26094.
Pawlak, Z. (1982). Rough Sets. International Journal of Computer & Information Sciences, 11(5), 341–356.
Raś, Z. W., & Wieczorkowska, A. (2000). Action-Rules: How to Increase Profit of a Company. In Proceedings of PKDD’00, Lyon, France, LNAI, (Vol. 1910 pp. 587–592): Springer.
Silow-Carroll, S., Edwards, J. N., & Lashbrook, A. (2011). Reducing hospital readmissions: Lessons from Top-Performing hospitals. CareManagement, 17(5), 14.
Sun, J., Wang, F., Hu, J., & Edabollahi, S. (2012). Supervised patient similarity measure of heterogeneous patient records. SIGKDD Explor. Newsl., 14(1), 16–24.
Sushil, M., Šuster, S., Luyckx, K., & Daelemans, W. (2018). Patient representation learning and interpretable evaluation using clinical notes. Journal of Biomedical Informatics, 84, 103–113.
Tarnowska, K. A., Ras, Z. W., & Jastreboff, P. J. (2017). Decision Support System for Diagnosis and Treatment of Hearing Disorders: the Case of Tinnitus. In Studies in Computational Intelligence, Vol. 685: Springer.
Touati, H., Raś, Z. W., Studnicki, J., & Wieczorkowska, A. A. (2014). In Foundations of Intelligent Systems, LNAI, (Vol. 8502 pp. 254–263): Springer.
Tremblay, M. C., Berndt, D. J., & Studnicki, J. (2006)). Feature Selection for Predicting Surgical Outcomes. In . HICSS’06. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, (Vol. 5 pp. 93a–93a): IEEE.
Yang, H., Li, W., Liu, K., & Zhang, J. (2012). Knowledge-based clinical pathway for medical quality improvement. Information Systems Frontiers, 14(1), 105–117.
Zhang, P., Wang, F., Hu, J., & Sorrentino, R. (2014). Towards personalized medicine: leveraging patient similarity and drug similarity analytics. AMIA Summits on Translational Science Proceedings, 2014, 132.
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This work was supported by SAS Institute under UNC-Charlotte Internal Grant No. 15-0645.
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Mardini, M.T., Raś, Z.W. Discovering Primary Medical Procedures and their Associations with Other Procedures in HCUP Data. Inf Syst Front 24, 133–147 (2022). https://doi.org/10.1007/s10796-020-10058-9
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DOI: https://doi.org/10.1007/s10796-020-10058-9