Abstract
The large availability of hospital administrative and clinical data has encouraged the application of Process Mining techniques to the healthcare domain. Predictive Process Monitoring techniques can be used in order to learn from these data related to past historical executions and predict the future of incomplete cases. However, some of these data, possibly the most informative ones, are often available in natural language text, while structured information—extracted from these data—would be more beneficial for training predictive models.
In this paper we focus on the scenario of the Home Hospitalization Service, supporting the team in making decisions on the home hospitalization of a patient, by predicting whether it is likely that a new patient will successfully undergo home hospitalization. We aim at investigating whether, in this scenario, we can take advantage of mapping unstructured textual diagnoses, reported by the doctor in the Emergency Department, into structured information, as the standardized disease ICD-9-CM codes, to provide more accurate predictions. To this aim, we devise two different approaches involving respectively lexicographic and semantic distance for mapping textual diagnoses in ICD-9-CM codes and leverage the structured information for making predictions.
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Notes
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We used snowball stemmer from nlkt package https://www.nltk.org/_modules/nltk/stem/snowball.html.
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The percentages in Table 1 refer to the number of mappings per diagnosis. Note that these are in principle different from the number of mappings per trace in which the diagnosis appears, since the same diagnosis may appear in more than one trace.
References
van der Aalst, W.M.P.: Process Mining - Data Science in Action. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Akshara, P., Shidharth, S., Gokul S., K., Sowmya, K.: Integrating structured and unstructured patient data for ICD9 disease code group prediction. In: 8th ACM IKDD CODS and 26th COMAD, p. 436. Association for Computing Machinery (2021)
van der Aalst, W., et al.: Process mining Manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19
Amantea, I.A., et al.: A process mining application for the analysis of hospital-at-home admissions. Stud. Health Technol. Inform. 270, 522–526 (2020)
Aringhieri, R., et al.: Leveraging structured data in predictive process monitoring: the case of the ICD-9-CM in the scenario of the home hospitalization service. In: Proceedings of the Workshop on Towards Smarter Health Care: Can Artificial Intelligence Help? Co-Located with AIxIA2021. CEUR Workshop Proceedings, vol. 3060, pp. 48–60. CEUR-WS.org (2021)
Bagheri, A., Sammani, A., Heijden, P.G., Asselbergs, F., Oberski, D.: Automatic ICD-10 classification of diseases from Dutch discharge letters, pp. 281–289, January 2020
Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1), 6 (2020)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of, NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics (2019)
Di Francescomarino, C., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. IEEE Trans. Serv. Comput. 12(6), 896–909 (2019)
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_27
Duarte, F., Martins, B., Pinto, C., Silva, M.: A deep learning method for ICD-10 coding of free-text death certificates, pp. 137–149, August 2017
Gangavarapu, T., Jayasimha, A., Krishnan, G.S., Kamath, S.: Predicting ICD-9 code groups with fuzzy similarity based supervised multi-label classification of unstructured clinical nursing notes. Knowl.-Based Syst. 190, 105321 (2020)
Gangavarapu, T., Krishnan, G.S., Kamath, S., Jeganathan, J.: Farsight: long-term disease prediction using unstructured clinical nursing notes. IEEE Trans. Emerg. Top. Comput. 9(3), 1151–1169 (2021)
Isaia, G., Bertone, P., Isaia, G.C., Ricauda, N.: Home care for patients with chronic obstructive pulmonary disease. Arch. Phys. Med. Rehabil. 100, 664–665 (2010)
Koopman, B., Zuccon, G., Nguyen, A., Bergheim, A., Grayson, N.: Automatic ICD-10 classification of cancers from free-text death certificates. Int. J. Med. Inform. 84 (2015)
Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_21
Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31
Matthews, B.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Struct. 405(2), 442–451 (1975)
Nkolele, R.: Mapping of narrative text fields to ICD-10 codes using natural language processing and machine learning. In: Proceedings of the The Fourth Widening Natural Language Processing Workshop, pp. 131–135. Association for Computational Linguistics, Seattle, July 2020
Pegoraro, M., Uysal, M.S., Georgi, D., Aalst, W.: Text-aware predictive monitoring of business processes, April 2021
Rizzi, W., Simonetto, L., Di Francescomarino, C., Ghidini, C., Kasekamp, T., Maggi, F.M.: Nirdizati 2.0: new features and redesigned backend. In: Demonstration Track at BPM 2019. CEUR Workshop Proceedings, vol. 2420, pp. 154–158. CEUR-WS.org (2019)
Sulis, E., et al.: Monitoring patients with fragilities in the context of de-hospitalization services: an ambient assisted living healthcare framework for e-health applications. In: 23rd ISCT, pp. 216–219. IEEE (2019)
Sulis, E., Terna, P., Di Leva, A., Boella, G., Boccuzzi, A.: Agent-oriented decision support system for business processes management with genetic algorithm optimization: an application in healthcare. J. Med. Syst. 44(9), 1–7 (2020)
Teinemaa, I., Dumas, M., Maggi, F.M., Di Francescomarino, C.: Predictive business process monitoring with structured and unstructured data. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 401–417. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_23
Verenich, I., Dumas, M., La Rosa, M., Maggi, F.M., Di Francescomarino, C.: Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 218–229. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_18
Yuan, Z., Zhao, Z., Sun, H., Li, J., Wang, F., Yu, S.: Coder: knowledge infused cross-lingual medical term embedding for term normalization (2021)
Acknowledgments
This research has been partially carried out within the “Circular Health for Industry” project, funded by “Compagnia San Paolo” under the call “Intelligenza Artificiale, uomo e società”.
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Ronzani, M. et al. (2022). Unstructured Data in Predictive Process Monitoring: Lexicographic and Semantic Mapping to ICD-9-CM Codes for the Home Hospitalization Service. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_48
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