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Reconstructing the patient's natural history from electronic health records.
Metadata
Journalartificial intelligence in medicine4.383Date
2020 May 03
5 months ago
Type
Research Support, Non-U.S. Gov't
Journal Article
Volume
2020-05 / 105 : 101860
Author
Najafabadipour M 1, Zanin M 2, Rodríguez-González A 3, Torrente M 4, Nuñez García B 5, Cruz Bermudez JL 6, Provencio M 7, Menasalvas E 8
Affiliation
  • 2. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain. Electronic address: [email protected]
  • 3. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain. Electronic address: [email protected]
  • 4. Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. Electronic address: [email protected]
  • 5. Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. Electronic address: [email protected]
  • 6. Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. Electronic address: [email protected]
  • 7. Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. Electronic address: [email protected]
  • 8. Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain. Electronic address: [email protected]
Doi
PMIDMESH
Abstract
The automatic extraction of a patient's natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient's medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information. In this paper, we introduce a new Natural Language Processing (NLP) framework, capable of extracting the aforementioned elements from EHRs written in Spanish using rule-based methods. We focus on building medical timelines, which include disease diagnosis and its progression over time. By using a large dataset of EHRs comprising information about patients suffering from lung cancer, we show that our framework has an adequate level of performance by correctly building the timeline for 843 patients from a pool of 989 patients, achieving a precision of 0.852.
Keywords: Electronic Health Records Natural Language Processing Temporal Reasoning
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Artif Intell Medartificial intelligence in medicine
Metadata
LocationNetherlands
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