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 an approach for mapping textual diagnoses in ICD-9-CM codes and leverage the structured information for making predictions.

Leveraging structured data in Predictive Process Monitoring: The case of the ICD-9-CM in the scenario of the Home Hospitalization Service

Aringhieri R.;Boella G.;Brunetti E.;Di Caro L.;Di Francescomarino C.;Ferrod R.;Marinello R.;Ronzani M.;Sulis E.
2021-01-01

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 an approach for mapping textual diagnoses in ICD-9-CM codes and leverage the structured information for making predictions.
2021
2021 Workshop on Towards Smarter Health Care: Can Artificial Intelligence Help?, SMARTERCARE 2021
Virtual online
2021
CEUR Workshop Proceedings
CEUR-WS
3060
48
60
Healthcare processes; Home hospitalization service; Natural language processing; Predictive process monitoring
Aringhieri R.; Boella G.; Brunetti E.; Di Caro L.; Di Francescomarino C.; Dragoni M.; Ferrod R.; Ghidini C.; Marinello R.; Ronzani M.; Sulis E....espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1889770
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