Analysis of Electronic Health Records to Identify the Patient's Treatment Lines: Challenges and Opportunities

Najafabadipour, Marjan and Tuñas, Juan Manuel and Rodríguez González, Alejandro and Menasalvas Ruiz, Ernestina (2019). Analysis of Electronic Health Records to Identify the Patient's Treatment Lines: Challenges and Opportunities. In: "39th SGAI International Conference on Artificial Intelligence, AI", December 17–19, 2019, Cambridge, UK. pp. 437-442. https://doi.org/10.1007/978-3-030-34885-4_33.

Description

Title: Analysis of Electronic Health Records to Identify the Patient's Treatment Lines: Challenges and Opportunities
Author/s:
  • Najafabadipour, Marjan
  • Tuñas, Juan Manuel
  • Rodríguez González, Alejandro
  • Menasalvas Ruiz, Ernestina
Item Type: Presentation at Congress or Conference (Article)
Event Title: 39th SGAI International Conference on Artificial Intelligence, AI
Event Dates: December 17–19, 2019
Event Location: Cambridge, UK
Title of Book: International Conference on Innovative Techniques and Applications of Artificial Intelligence SGAI 2019: Artificial Intelligence XXXVI
Date: 2019
ISSN: 0000-0000
Volume: 11927
Subjects:
Freetext Keywords: Electronic Health Records; Natural Language Processing; Named Entity Recognition; Temporal relation identification
Faculty: Centro de Tecnología Biomédica (CTB) (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The automatic reconstruction of the patient´s treatment lines from their Electronic Health Records (EHRs) is a significant step towards improving the quality and the safety of the healthcare deliveries. With the recent rapid increase in the adaption of EHRs and the rapid development of computational science, we can discover new insights from the information stored in EHRs. However, this is still a challenging task, being unstructured data analysis one of them. In this paper, we focus on the most common challenges for reconstructing the patient´s treatment lines, which are the Named Entity Recognition (NER), temporal relation identification and the integration of structured results. We introduce our Natural Language Processing (NLP) framework, which deals with the aforementioned challenges. In addition, we focus on a real use case of patients, suffering from lung cancer to extract patterns associated with the treatment of the disease that can help clinicians to analyze toxicities and patterns depending on the lines of treatments given to the patient.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020727658IASISNCSRD - National Center for Scientific Research DEMOKRITOSIntegration and analysis of heterogeneous big data for precision medicine and suggested treatments for different type of patients

More information

Item ID: 63978
DC Identifier: https://oa.upm.es/63978/
OAI Identifier: oai:oa.upm.es:63978
DOI: 10.1007/978-3-030-34885-4_33
Official URL: https://link.springer.com/chapter/10.1007/978-3-030-34885-4_33
Deposited by: Memoria Investigacion
Deposited on: 22 Sep 2020 07:46
Last Modified: 22 Sep 2020 07:46
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