@inproceedings{torri-etal-2024-structuring,
title = "Structuring Clinical Notes of {I}talian {ST}-elevation Myocardial Infarction Patients",
author = "Torri, Vittorio and
Mazzucato, Sara and
Dalmiani, Stefano and
Paradossi, Umberto and
Passino, Claudio and
Moccia, Sara and
Micera, Silvestro and
Ieva, Francesca",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Thompson, Paul and
Ondov, Brian",
booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.cl4health-1.5",
pages = "37--43",
abstract = "In recent years, it has become common for patients to get full access to their Electronic Health Records (EHRs), thanks to the advancements in the EHRs systems of many healthcare providers. While this access empowers patients and doctors with comprehensive and real-time health information, it also introduces new challenges, in particular due to the unstructured nature of much of the information within EHRs. To address this, we propose a pipeline to structure clinical notes, providing them with a clear and concise overview of their health data and its longitudinal evolution, also allowing clinicians to focus more on patient care during consultations. In this paper, we present preliminary results on extracting structured information from anamneses of patients diagnosed with ST-Elevation Myocardial Infarction from an Italian hospital. Our pipeline exploits text classification models to extract relevant clinical variables, comparing rule-based, recurrent neural network and BERT-based models. While various approaches utilized ontologies or knowledge graphs for Italian data, our work represents the first attempt to develop this type of pipeline. The results for the extraction of most variables are satisfactory (f1-score {\textgreater} 0.80), with the exception of the most rare values of certain variables, for which we propose future research directions to investigate.",
}
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<abstract>In recent years, it has become common for patients to get full access to their Electronic Health Records (EHRs), thanks to the advancements in the EHRs systems of many healthcare providers. While this access empowers patients and doctors with comprehensive and real-time health information, it also introduces new challenges, in particular due to the unstructured nature of much of the information within EHRs. To address this, we propose a pipeline to structure clinical notes, providing them with a clear and concise overview of their health data and its longitudinal evolution, also allowing clinicians to focus more on patient care during consultations. In this paper, we present preliminary results on extracting structured information from anamneses of patients diagnosed with ST-Elevation Myocardial Infarction from an Italian hospital. Our pipeline exploits text classification models to extract relevant clinical variables, comparing rule-based, recurrent neural network and BERT-based models. While various approaches utilized ontologies or knowledge graphs for Italian data, our work represents the first attempt to develop this type of pipeline. The results for the extraction of most variables are satisfactory (f1-score \textgreater 0.80), with the exception of the most rare values of certain variables, for which we propose future research directions to investigate.</abstract>
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%0 Conference Proceedings
%T Structuring Clinical Notes of Italian ST-elevation Myocardial Infarction Patients
%A Torri, Vittorio
%A Mazzucato, Sara
%A Dalmiani, Stefano
%A Paradossi, Umberto
%A Passino, Claudio
%A Moccia, Sara
%A Micera, Silvestro
%A Ieva, Francesca
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Thompson, Paul
%Y Ondov, Brian
%S Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F torri-etal-2024-structuring
%X In recent years, it has become common for patients to get full access to their Electronic Health Records (EHRs), thanks to the advancements in the EHRs systems of many healthcare providers. While this access empowers patients and doctors with comprehensive and real-time health information, it also introduces new challenges, in particular due to the unstructured nature of much of the information within EHRs. To address this, we propose a pipeline to structure clinical notes, providing them with a clear and concise overview of their health data and its longitudinal evolution, also allowing clinicians to focus more on patient care during consultations. In this paper, we present preliminary results on extracting structured information from anamneses of patients diagnosed with ST-Elevation Myocardial Infarction from an Italian hospital. Our pipeline exploits text classification models to extract relevant clinical variables, comparing rule-based, recurrent neural network and BERT-based models. While various approaches utilized ontologies or knowledge graphs for Italian data, our work represents the first attempt to develop this type of pipeline. The results for the extraction of most variables are satisfactory (f1-score \textgreater 0.80), with the exception of the most rare values of certain variables, for which we propose future research directions to investigate.
%U https://aclanthology.org/2024.cl4health-1.5
%P 37-43
Markdown (Informal)
[Structuring Clinical Notes of Italian ST-elevation Myocardial Infarction Patients](https://aclanthology.org/2024.cl4health-1.5) (Torri et al., CL4Health-WS 2024)
ACL
- Vittorio Torri, Sara Mazzucato, Stefano Dalmiani, Umberto Paradossi, Claudio Passino, Sara Moccia, Silvestro Micera, and Francesca Ieva. 2024. Structuring Clinical Notes of Italian ST-elevation Myocardial Infarction Patients. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, pages 37–43, Torino, Italia. ELRA and ICCL.